NOISE ADAPTATION IN A HIDDEN MARKOV MODEL
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`SPEECH RECOGNITION SYSTEM
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`Dirk Van Compernolle(cid:0)
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`Department of Electrical Engineering (cid:2) ESAT y
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`Katholieke Universiteit Leuven
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`Kardinaal Mercierlaan 
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`B(cid:2) Heverlee
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`Belgium
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`Tel (cid:7) (cid:8)(cid:9) (cid:11) (cid:14)(cid:15) (cid:15)
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`Abstract
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`Several ways for making the signal processing in an isolated word speech recognition
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`system more robust against large variations in the background noise level are presented(cid:2)
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`Isolated word recognition systems are sensitive to accurate silence detection(cid:3) and are eas(cid:4)
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`ily overtrained on the speci(cid:5)c noise circumstances of the training environment(cid:2) Spectral
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`subtraction provides good noise immunity in the cases where the noise level is lower or
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`slightly higher in the testing environment than during training(cid:2) Di(cid:6)erences in residual
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`noise energy after spectral subtraction between a clean training and noisy testing envi(cid:4)
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`ronment can still cause severe problems(cid:2) The usability of spectral subtraction is largely
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`increased if complemented with some extra noise immunity processing(cid:2) This is achieved by
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`the addition of arti(cid:5)cial noise after spectral subtraction or by adaptively re(cid:4)estimating the
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`(cid:0)Research Associate of the National Fund for Scienti(cid:2)c Research of Belgium (cid:3)N(cid:4)F(cid:4)W(cid:4)O(cid:4)(cid:5)
`yThe work was performed while the author was at IBM T(cid:4)J(cid:4) Watson Research Center(cid:6) Yorktown Heights(cid:6)
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`NY  (cid:4)
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 1
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`noise statistics during a training session(cid:2) Both techniques are almost equally successful in
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`dealing with the noise(cid:2) Noise addition achieves the additional robustness that the system
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`will never be allowed to learn about low amplitude events(cid:3) that might not be observable
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`in all environments(cid:7) this(cid:3) however(cid:3) at a cost that some information is consistently thrown
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`away in the most favorable noise situations(cid:2)
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 2
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`I(cid:2) Introduction
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`The signal processing (cid:3) (cid:4) used in IBM(cid:5)s real(cid:6)time speech recognizer (cid:3)(cid:4)(cid:7) has performed well
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`in controlled environments(cid:8) At the time of development the system was exclusively used in
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`a quiet o(cid:9)ce with a typical signal to noise ratio of about dB(cid:8) Day to day variations in a
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`speakers voice level had to be accounted for(cid:7) but the acoustic environment changed little(cid:8)
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`The development of the portable Tangora system (cid:3) (cid:4) allowed for use of the recognition
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`system in a place at the convenience of the user(cid:7) but simultaneously created a new set of
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`problems(cid:8) It became soon clear that the existing signal processing was not able to deal with
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`the large variations in acoustic backgrounds that the system was now exposed to(cid:8) Sensitivity
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`to the absolute noise level was moderate(cid:7) i(cid:8)e(cid:8) when the system was tested in the same acoustic
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`environment as the one in which it was trained(cid:8) Under those conditions a drop in signal to
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`noise ratio from  to dB corresponded to a doubling in error rate(cid:8) But a di(cid:13)erence in
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`the signal to noise ratio larger than dB(cid:7) between the training and testing environment lead
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`to severe deterioration in performance(cid:8) Using a lip(cid:6)mike can in principle solve most noise
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`problems in o(cid:9)ce applications(cid:8) The goal at IBM has been(cid:7) however(cid:7) to use the recognizer
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`with a less constraining table mounted microphone(cid:8)
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`A (cid:15)silence model(cid:15)(cid:7) representing the short pauses between words(cid:7) is one of the hidden
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`Markov models used in the isolated word recognition system(cid:8) The inclusion of the silence
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`model as a regular Hidden Markov Model (cid:16)HMM(cid:17) is necessary to avoid the otherwise di(cid:9)cult
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`task of endpoint detection(cid:8) It is trained in conjunction with the speech models and hence
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`learns about the noise characteristics of the training session(cid:8) Large di(cid:13)erences in the noise
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`level of training and testing environments will cause the recognizer to mistake speech for noise
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`or noise for speech(cid:8) Two rules for robust signal processing for speech recognition emerge (cid:18) the
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`processing of speech events must be largely invariant to the noise level and noise(cid:7) independent
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`of its actual value(cid:7) should be mapped into a typical noise image(cid:8) This is a normalization and
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`adaptation task and therefore quite distinct from actual noise removal(cid:7) which is the goal in
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`speech enhancement (cid:3)(cid:7)(cid:4)(cid:8)
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 3
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`The approach taken in this work was to maintain the general signal processing structure of
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`the existing system(cid:7) which has proven successful across many speakers in controlled constant
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`acoustic environments(cid:7) and to complement it with noise immunity processing that is largely
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`transparent for the low noise environments but e(cid:13)ective in dealing with changes in the back(cid:6)
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`ground noise level(cid:8) In section II the signal processing of the IBM speech recognition system
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`is reviewed(cid:8) In section III we introduce the basic noise immunity components of the signal
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`processing which are spectral subtraction and a frequency dependent channel equalizer(cid:8) Both
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`operations rely heavily on a histogram based speech(cid:20)noise discrimination algorithm(cid:8) Re(cid:21)ne(cid:6)
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`ments to the noise immunity signal processing and the statistical silence model are explored
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`in sections IV and V(cid:8) Ultimately in section VI results obtained on the IBM speech recognition
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`system for all of the presented schemes are given and analyzed(cid:8)
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`II(cid:2) Signal Processing Overview
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`The signal processing (cid:16)Fig(cid:8) (cid:17) converts an input signal(cid:7) sampled at kHz(cid:7) into a  di(cid:6)
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`mensional output vector at a frame rate of frames(cid:20)sec(cid:8) It can be divided into  parts (cid:18)
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`Fourier transform(cid:7) simulated (cid:21)lterbank(cid:7) log conversion(cid:7) long term adaptation and short term
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`adaptation(cid:8)
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`Figure (cid:18) Signal processing block diagram (cid:16)SIGPSTD(cid:17)
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`All blocks(cid:7) except for the long term adaptation(cid:7) are identical to the design described in
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`(cid:3) (cid:4)(cid:8) The FFT uses a   point Hanning window and creates a new spectral vector every
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` msec(cid:8) A mel(cid:6)scaled (cid:21)lterbank is simulated by adding up FFT power spectrum coe(cid:9)cients(cid:8)
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` channels spanning the frequency range from Hz to Hz are created(cid:8) The long term
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`adaptation(cid:7) which is described in detail in the next section plays a normalization role(cid:8) Its
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`output is ideally independent of the current acoustic environment(cid:8) The short term adaptation
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`is based on a Schroeder(cid:6)Hall haircell model (cid:3)(cid:4)(cid:8) It is modeled after the rapid adaptation seen
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 4
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`in neural (cid:21)ring rate according to changes in input level(cid:8) The time constants(cid:7) dependent on
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`the actual input(cid:7) are between and  msec(cid:8) The importance of this block for the stationary
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`parts of speech is minimal(cid:7) but quite signi(cid:21)cant for the transient parts(cid:8)
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`The  dimensional output vector is labeled by a vector quantizer with a codebook of
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`size (cid:8) These labels are the inputs to the HMM based recognition system (cid:3)(cid:4)(cid:8) The VQ
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`codebook is designed by K(cid:6)means clustering of training data (cid:3)(cid:4)(cid:8) This particular way of
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`labeling(cid:7) especially the use of a Euclidean distance metric in (cid:21)nding a closest prototype has
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`a direct impact on the signal processing(cid:8)
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`III(cid:2) Noise Correction and Acoustic Adaptation(cid:2)
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`The function of the signal processing block (cid:15)long term adaptation(cid:15) is to compensate for the
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`large day to day variations in the environment(cid:7) and map the observed variable dynamic ranges
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`into a (cid:21)xed dynamic range about which the system is allowed to learn(cid:8) The variations include
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`changes in the background noise(cid:7) room acoustics and recording hardware(cid:8)
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`X i
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`(cid:2)
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`(cid:3)i
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`raw power spectral estimate
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`rms noise threshold
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`noise estimate
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`SST HRi
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`spectral subtraction threshold
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`Gi
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`yi
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`channel gain
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`adapted spectral estimate
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`Figure (cid:18) Basic long term adaptation for a single channel (cid:16)NOISIM (cid:17)
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`The system proposed here (cid:16)Fig(cid:8) (cid:17) splits the correction for the noise and the adaptation
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`to the room acoustics into two largely independent parts(cid:7) but some linkage between both
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` Notation (cid:12) Capital letters are used for power spectrum variables(cid:6) small letters for log spectrum variables(cid:4)
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`Channel numbers are given as superscripts(cid:4)
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 5
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`parts is crucial to the overall success(cid:8) Spectral subtraction (cid:3)(cid:4) is used for the noise correc(cid:6)
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`tion(cid:8) A channel equalization(cid:7) which is somewhat like a frequency dependent automatic gain
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`control(cid:7) compensates for the variable microphone and room acoustic transferfunctions(cid:8) Both
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`operations need a reasonably good noise(cid:20)speech discriminator(cid:7) which in this work is based on
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`signal rms energy only(cid:8)
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`No e(cid:13)ort is made within the signal processing to remove impulsive noises(cid:7) such as slamming
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`doors(cid:7) coughing etc(cid:8) from the signal(cid:8) Hence all underlying parameters can truly be modeled
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`as slowly varying(cid:8) Before any long term adaptation processing(cid:7) histograms are collected of
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`the total and single channel log(cid:6)rms energies as shown in Fig(cid:8) (cid:8) Long term adaptation
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`parameters are derived from these histograms or from running averages(cid:8) Parameter estimates
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`are updated in block mode at regular intervals(cid:7) typically secs(cid:7) at which time the histograms
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`are also restarted(cid:8)
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`Use of Log(cid:2)rms Histograms(cid:3) Rms signal energy is computed over the frequency range
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`used by the recognizer (cid:16)Hz(cid:6)Hz(cid:17)(cid:8) Histograms of the log of signal energy typically
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`have a clean bimodal distribution (cid:3)(cid:4)(cid:7) corresponding to (cid:15)noise only(cid:15) or (cid:15)noise (cid:25) speech(cid:15)(cid:26)
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`these can be considered as the two basic states of the system(cid:8) The noise contribution to
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`the histograms and to a lesser extend the speech contribution tends to be modeled well by
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`a normal distribution (cid:16)Fig(cid:8) (cid:17)(cid:8) This modeling has as basic assumption that speech(cid:7) when
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`present(cid:7) largely dominates the noise(cid:8) Hence it is only suitable for applications with signi(cid:21)cant
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`positive signal to noise ratios(cid:8)
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`Using index (cid:15)no(cid:15) for the noise distribution and (cid:15)sp(cid:15) for the speech distribution(cid:7) the joint
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`probabilities of state and current log(cid:6)rms observation et(cid:7) can be written as (cid:18)
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`pk(cid:16)et(cid:17) (cid:27)
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`(cid:4)kp(cid:5)(cid:6)k
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`e
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`(cid:2)
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`(cid:0) et(cid:2)(cid:2)k(cid:3)k (cid:2)
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`k (cid:27) no(cid:7) sp
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`(cid:16) (cid:17)
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`which now allows for writing the conditional probability of being in silence(cid:7) given an rms
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`observation as (cid:18)
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`p(cid:16)nojet(cid:17) (cid:27)
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`pno(cid:16)et(cid:17)
`pno(cid:16)et(cid:17) (cid:25)p sp(cid:16)et(cid:17)
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`(cid:16)(cid:17)
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 6
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`and also (cid:18)
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`p(cid:16)spjet(cid:17) (cid:27) (cid:2) p(cid:16)nojet(cid:17)
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`Figure (cid:18) Log(cid:6)rms histogram (cid:21)tted with  gaussians
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`The generating mixing ratio(cid:7) means and variances (cid:16)(cid:4)k(cid:7) (cid:3)k and (cid:6)k(cid:17) of the best (cid:21)t to the
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`histogram with a mixture of  gaussians can be found using a standard EM procedure (cid:3)(cid:4)(cid:8) A
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`speech(cid:20)noise decision threshold (cid:2) is derived from this (cid:21)t as the point of equal probability on
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`the two gaussian distributions (cid:18)
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`p(cid:16)noj(cid:2)(cid:17) (cid:27) p(cid:16)spj(cid:2)(cid:17) (cid:27) (cid:8)
`pno(cid:16)(cid:2)(cid:17) (cid:27) psp(cid:16)(cid:2)(cid:17)
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`The detection of a noise frame can conveniently be written as (cid:18)
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`(cid:9)no
`t
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`(cid:27)
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`if et (cid:10) (cid:2)
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`(cid:27)
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`if et (cid:3) (cid:2)
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`(cid:16) (cid:8)a(cid:17)
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`(cid:16) (cid:8)b(cid:17)
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`(cid:16)(cid:8)a(cid:17)
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`(cid:16)(cid:8)b(cid:17)
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`This decision criterion is de(cid:21)nitely not perfect on a frame by frame basis and would not be
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`suitable for the purpose of word boundary detection(cid:8) But su(cid:9)ciently few errors are made(cid:7) such
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`that on the basis of it excellent statistics can be gathered about the noise environment(cid:8) An
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`additional advantage is that the described derivation of (cid:2) is suited for a mixed input consisting
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`of noise and speech(cid:7) and that it does not require occasional long pauses for measuring noise
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`statistics(cid:7) nor is any previous knowledge about the noise level required(cid:8) In an isolated word
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`speech recognition system(cid:7) pauses in between words and sentences typically take up to 
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`(cid:28) of all input(cid:7) hence providing ample information about the environment(cid:8)
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`Spectral Subtraction(cid:3)
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`The noise estimate for spectral subtraction is computed from
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`the running average of power spectra of frames that are classi(cid:21)ed as noise by the rms decision
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 7
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`threshold(cid:8) The updates are done in block mode for synchrony with the other parameter
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`updates derived in the next section (cid:18)
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`t
`(cid:3)i (cid:27) (cid:11) (cid:4) Pt X it (cid:9)no
`Pt (cid:9)no
`in which the choice of (cid:11) and the block width determine the e(cid:13)ective time constant(cid:8) As spectral
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`
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`t
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`(cid:25) (cid:16) (cid:2) (cid:11)(cid:17) (cid:4) (cid:3)i
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`f or i (cid:27) (cid:7) 
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`(cid:16)(cid:17)
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`subtraction is a power domain operation(cid:7) power spectrum averages are used in computing the
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`bias(cid:8) The signal spectral estimate(cid:7) with the noise removed(cid:7) is obtained as (cid:18)
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`(cid:29)X i (cid:27) max(cid:16)X i (cid:2) (cid:3)i(cid:7) SST HRi(cid:17)
`The spectral subtraction threshold SST HRi must be chosen larger than or equal to (cid:7)
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`f or i (cid:27) (cid:7) 
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`(cid:16)(cid:17)
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`because power spectra can never be negative(cid:8) The choice of its actual value is very important
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`and how it is derived on a channel dependent basis is discussed in a following section(cid:8)
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`Figure (cid:18) Spectral subtraction (cid:18) input(cid:20)output relationship in the log domain
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`Estimating the Frequency Dependent Gains(cid:3)
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`Simultaneously with the log(cid:6)rms his(cid:6)
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`togram(cid:7) histograms are gathered of log(cid:6)energy in each channel(cid:8) The (cid:15)Channel Peak Energy(cid:15)
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`(cid:16)CP Ei(cid:17)(cid:7) a measurement for the highest energy that can be expected in a given frequency
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`channel(cid:7) is de(cid:21)ned as the (cid:8) (cid:28) upper percentile on the speech distribution of a channel his(cid:6)
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`togram(cid:8) The contribution of speech and noise to a histogram is derived on basis of the rms
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`decision criterion(cid:8) Individual channel gains are computed as (cid:18)
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`Gi (cid:27) T ARGET (cid:2) CP Ei
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`and implemented as simple additions to the log spectra (cid:18)
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`yi (cid:27) log
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`(cid:29)X i (cid:25) Gi
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`(cid:16)(cid:17)
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`(cid:16)(cid:17)
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`If histograms and their peaks were to be recomputed after long term adaption(cid:7) then all
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`channel peak energies would be identical to the selected T ARGET value(cid:7) from there the name
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`(cid:15)equalization(cid:15) in Fig(cid:8) (cid:8)
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 8
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`The idea behind this derivation of channel gains is that long observations of speech will
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`roughly have equal frequency peaks across the whole speech frequency range(cid:8) The peaks of the
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`histograms can therefore be considered as a measurement of the combined transferfunction of
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`the environment and the recording equipment rather than as a measurement of the utterance(cid:8)
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`Figure (cid:18) Spectral subtraction (cid:18) impact on channel histograms
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`Choosing the Spectral Subtraction Threshold(cid:3) The importance of the spectral sub(cid:6)
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`traction threshold SST HRi (cid:16)(cid:17) has been discussed at some length in (cid:3) (cid:4)(cid:8) Most noise values
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`are mapped into this value because of the inherent clipping in spectral subtraction(cid:8) The
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`chosen threshold value(cid:7) modi(cid:21)ed by the channel gain of the equalization(cid:7) is the (cid:15)typical noise
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`image(cid:15) of the channel(cid:7) after long term adaptation(cid:8) As the peak energy at the output of long
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`term adaptation is already (cid:21)xed at the T ARGET value it is su(cid:9)cient to a (cid:21)xed output dy(cid:6)
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`namic range in order to create the desired environment independent noise image(cid:8) Hence the
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`channel equalization (cid:16)(cid:17) must be anticipated by setting the spectral subtraction threshold at
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`a (cid:21)xed level (cid:16)DY N (cid:17) below the channel peak energy (cid:18)
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` log (cid:16)SST HRi(cid:17) (cid:27) CP Ei (cid:2) DY N
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`(cid:16) (cid:17)
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`It remains to choose an appropriate value for DY N in (cid:16) (cid:17)(cid:8) The distortion(cid:7) introduced by
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`spectral subtraction (cid:16)(cid:17)(cid:7) is small if the threshold is chosen close to the original noise value(cid:8)
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`But because DY N must be (cid:21)xed in all circumstances(cid:7) this can only be done for one envi(cid:6)
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`ronment(cid:8) The quiet environment with a typical dynamic range in our applications of roughly
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`dB is the most obvious candidate(cid:8) The I(cid:20)O relationship for spectral subtraction(cid:7) including
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`this particular choice of the spectral subtraction threshold(cid:7) is illustrated in Fig(cid:8)  on a log(cid:6)log
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`scale(cid:8) In the example the channel peak energy is dB and the noise estimate dB(cid:7) which cor(cid:6)
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`responds to a dB dynamic range in this channel(cid:8) This is typical for a noisy(cid:7) though not bad(cid:7)
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`environment(cid:8) Fig(cid:8)  shows the e(cid:13)ect of spectral subtraction on a single channel histogram(cid:7)
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 9
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`assuming histograms are computed immediately before and after spectral subtraction(cid:8) The
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`thresholding produces a very large peak at the spectral subtraction threshold(cid:8) Immediately
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`above it there is a very sparse region(cid:7) due to the dynamic range stretching(cid:8) Inputs more than
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`dB above the noise estimate are hardly a(cid:13)ected(cid:8) If the original dynamic range is dB or
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`more(cid:7) then the noise region will be compressed instead of expanded(cid:8)
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`IV(cid:2) Additional Noise Immunity Processing
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`Long term adaptation using only spectral subtraction and equalization proved to be a great
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`step forward in terms of noise immunity(cid:7) and satis(cid:21)ed the goal of introducing no degradation
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`for the noise free environments(cid:7) as results will show(cid:8) However the impact on the recognition
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`system is insu(cid:9)cient for the most demanding situations(cid:8) Fig(cid:8)  shows signi(cid:21)cant stretching
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`of the dynamic range for noisy situations(cid:8) Residual noise (cid:16)output for non(cid:6)thresholded noise
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`input(cid:17) can hence be quite di(cid:13)erent form the typical noise image(cid:8) Roughly out of  channels
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`contain residual noise(cid:8) The Euclidean distance between residual noise of a clean and noisy
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`environment can be large(cid:7) leading to frequent labeling errors(cid:8) This problem is acute for a
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`quiet training environment and a very noisy testing environment(cid:8)
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`Two additional components are introduced in the long term adaptation to deal with these
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`extreme environments (cid:16)Fig(cid:8) (cid:17)(cid:8) They will have a slight negative in(cid:30)uence on the optimal
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`performance of the system(cid:7) but will further enhance the usability over di(cid:13)erent environments(cid:8)
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`Noise estimate and channel peak energy computations are not shown in Fig(cid:8) (cid:7) but are
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`identical to the ones in Fig(cid:8) (cid:8)
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`Figure (cid:18) Long term adaptation with additional noise immunity processing (cid:16)NOISIM(cid:17)
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`Noise Addition The (cid:21)rst addition to long term adaptation is noise masking(cid:8) From previous
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`experiments we know that the IBM speech recognition system performs well in situations
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 10
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`with a less than optimal dynamic range (cid:3) (cid:4)(cid:7) given that a better than dB SNR is retained(cid:8)
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`Noise immunity could conceivably be improved by adding arti(cid:21)cial noise into the system in a
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`controlled manner(cid:7) which can easily be done after the channel equalization (cid:16)Fig(cid:8) (cid:17) (cid:18)
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`and (cid:18)
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`zi (cid:27) log (cid:16)
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`yi
` (cid:25) n(cid:17)
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` log n (cid:5) N (cid:16)(cid:3)A(cid:7) (cid:6)
`A(cid:17)
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`(cid:16) (cid:17)
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`(cid:16) (cid:17)
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`Hence noise is added directly in the frequency domain(cid:8) The distribution chosen for the
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`additive noise is log(cid:6)normal(cid:7) which closely resembles natural background noise(cid:8) This noise
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`masking has a dual e(cid:13)ect(cid:8) First(cid:7) it masks out most of the noise residuals(cid:7) which are still
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`environment dependent(cid:8) Second(cid:7) it masks out low level events about which the system might
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`learn when trained in a clean environment but is unable to observe and use in a noisier one(cid:8)
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`One could consider a system based on noise masking and channel equalization solely(cid:7)
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`without spectral subtraction(cid:8) For e(cid:9)cient masking however(cid:7) the selected mean noise level (cid:3)A
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`must be several dB above the natural noise (cid:30)oor of all anticipated environments(cid:8) The level
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`required for e(cid:9)ciently masking the noise residuals in spectral subtraction is much lower (cid:3) (cid:4)(cid:8)
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`Noise Sharpening(cid:3)
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`A second improvement comes from using cross channel correlation
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`information(cid:8) Spectral subtraction(cid:7) as described before(cid:7) is a single channel operation in nature(cid:8)
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`But the log(cid:6)rms histograms have a much cleaner bimodal distribution than single channel
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`histograms(cid:7) and signi(cid:21)cantly smaller noise variances(cid:7) indicating high correlation between noisy
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`observations(cid:8) Noise sharpening exploits this correlation by modifying the power spectra based
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`on the global probability of being in a noise or in a speech region (cid:18)
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`i
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`X
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`(cid:27) p(cid:16)nojet(cid:17) (cid:4) (cid:3)i (cid:25) p(cid:16)spjet(cid:17) (cid:4) X i
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`(cid:16) (cid:17)
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`The probability that a frame at time t with log(cid:6)rms value et is noise is given by (cid:16) (cid:7)(cid:17)(cid:8) The
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`variance of the single channel noise distributions(cid:7) before spectral subtraction(cid:7) is reduced by
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`this noise sharpening(cid:8) This then results in a reduced variance of the noise residual distribution(cid:8)
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 11
`
`

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`Most of the speech frames are barely a(cid:13)ected by (cid:16) (cid:17)(cid:7) but a few low amplitude speech events
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`can be in(cid:30)uenced in an unfavorable way(cid:8)
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`V(cid:2) Re(cid:3)estimation of Noise Statistics
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`The fact that in some cases noise labels produced during testing are a poor match to the
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`ones on which the hidden Markov models were trained(cid:7) is not necessarily a problem(cid:8) The
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`only basic requirement for a robust system is that a string of noise labels can be distinguished
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`from any speech sound(cid:8) The fact that current hidden Markov model systems(cid:7) as the IBM one(cid:7)
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`use a single training session is not an optimal but pragmatic design(cid:8) Fully adaptive systems(cid:7)
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`that continuously update their statistics when in use(cid:7) are the obvious goal(cid:8) Several problems(cid:7)
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`both computational and conceptual ones(cid:7) remain to be solved before such a system will be
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`available(cid:8) Adaptively estimating speech statistics is di(cid:9)cult because an alignment has to be
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`made against utterances that are only known with a limited degree of accuracy(cid:8) However the
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`problem of adapting noise statistics only is much simpler(cid:7) because rather robust information
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`for noise vs(cid:8) speech discrimination is readily available from the rms and the rms decision
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`threshold(cid:8)
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`Anticipation of Unseen Events(cid:3)
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`The realization that the signal processing is not
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`perfect in creating its single noise image leads to a (cid:21)rst enhancement(cid:8) Noise residuals exist
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`and do depend on the actual noise level(cid:8) This imperfection can be compensated for by
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`assigning signi(cid:21)cant probabilities to all labels for the HMM silence model(cid:7) including the ones
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`that were not observed during training(cid:8) Adding uncertainty into a system in order to enhance
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`its robustness has to be used with care(cid:7) but can be very helpful(cid:8)
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`Adaptively Re(cid:2)estimating the Noise Probabilities(cid:3)
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`For the purpose of re(cid:6)estimating
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`the noise statistics the probability that a given frame is noise is again derived from the
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`binormal (cid:21)t to the rms histogram(cid:7) on basis of Eqs(cid:8) (cid:16) (cid:7)(cid:17)(cid:8) If we further use (cid:18)
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`(cid:9)j
`t (cid:27)
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`if label Lj was observed at time t
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 12
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`(cid:27)
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`otherwise
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`then a good estimate for the output probability of label Lj for the silence model is (cid:18)
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`P (cid:16)Ljjno(cid:17) (cid:27) Pt (cid:9)j
`t (cid:4) p(cid:16)nojet(cid:17)
`Pt p(cid:16)nojet(cid:17)
`The summation in (cid:16) (cid:17) typically extends over to a few sentences(cid:8) These new statistics
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`(cid:16) (cid:17)
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`are smoothed with the original ones derived from full HMM training(cid:8)
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`VI(cid:2) Experiments
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`Test data(cid:3)
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`The described algorithms were tested on the IBM isolated word speech
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`recognition system using a  word o(cid:9)ce correspondence vocabulary(cid:8) A typical experiment
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`has a training script of sentences containing words and a test script of  sentences(cid:7)
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`containing  words(cid:8) Two sets of recordings(cid:7) each set containing both training and testing(cid:7)
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`were used from (cid:21)ve speakers(cid:7) three male (cid:16)MAP(cid:7) PDM and ABK(cid:17) and two female (cid:16)NBD and
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`MAM(cid:17)(cid:8)
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`One set was made in a rather noise free environment and will be referred to as (cid:15)CLEAN(cid:15)(cid:7)
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`the other was made in a noisy laboratory and will be referred to as (cid:15)NOISY(cid:15)(cid:8) Di(cid:13)erences
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`between training and testing environment for the same condition and speaker are small(cid:8) The
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`clean environment has a signal to noise ratio varying from  to dB(cid:7) the noisy one typically
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`has a  to dB signal to noise ratio (cid:16)Table (cid:17)(cid:8) All recordings were made using a Crown
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`PZM S table mounted microphone(cid:26) no special e(cid:13)ort was made to control the acoustics(cid:8)
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`Signal to noise ratio (cid:16)SNR(cid:17)(cid:7) as always(cid:7) is a subjective measure(cid:8) For a single channel it
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`is de(cid:21)ned as the di(cid:13)erence between the channel peak energy and the noise estimate(cid:8) For
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`the full signal (cid:16)Table (cid:17) it is de(cid:21)ned as the average over the  channels(cid:8) Di(cid:13)erences of
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` to dB in SNR between channels are common(cid:8) The observed variations during a single
`
`recording session tend to be small compared to the gross changes between sessions(cid:8) Hence
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`The noisy background for MAP was arti(cid:2)cially achieved by adding (cid:13)f noise to the clean speech about
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` dB above the natural noise level(cid:4)
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`
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 13
`
`

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`ENVIR MAP PDM NBD MAM ABK
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`CLEAN
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`NOISY
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`Table (cid:18) Typical signal to noise ratios for di(cid:13)erent environments for the  speakers(cid:8)
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`the experimental results presented are indicative of how well an algorithm can deal with gross
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`day to day changes(cid:7) but not of how quickly it can adapt(cid:8) Fig(cid:8)  shows the range of values for
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`channel peak energies and noise estimates for the noisy test corpus for PDM(cid:8)
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`Figure (cid:18) Range of channel peak energies and noise estimates for the NOISY testing corpus
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`for speaker PDM(cid:8)
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`Results obtained with the older system without noise immunity processing (cid:16)SIGPSTD(cid:17)
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`in a constant environment are used as reference(cid:8) NOISIM and NOISIM are used to refer
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`to the signal processing schemes incorporating the long term adaptation of Figs(cid:8)  and 
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`respectively(cid:8) In all experiments the histograms are restarted after every secs of (cid:16)presumed(cid:17)
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` 
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 14
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`

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`speech(cid:8) The parameters derived from them are smoothed on an equal weight basis with the
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`previous estimates(cid:7) yielding e(cid:13)ective time constants of  to  secs depending on the mixing
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`ratio of speech and silence(cid:8)
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`Representation of Results(cid:3)
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`For each di(cid:13)erent algorithm the number of errors for each
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`speaker is reported in the  word task(cid:7) plus the average percentage error rate for  speakers(cid:8)
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`The error rates given are for substitution and deletion errors combined(cid:26) substitution errors
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`are generally the dominating source of errors(cid:8) Insertion errors also do occur(cid:7) but typically
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`at a rate of (cid:20) or less of the substitution error rate(cid:8) In some of the more demanding noise
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`environments noise gets su(cid:9)ciently mistaken for speech and higher insertions rates do occur(cid:26)
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`this is indicated in the results by (cid:16)(cid:25)(cid:17)(cid:8) Also the average decoding time for an experiment for
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`the (cid:21)ve speakers is given in CPU minutes on an IBM mainframe (cid:8) The error rate is the
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`more important criterion on which an algorithm will be judged(cid:7) but the decoding time also
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`gives an idea of its e(cid:9)ciency(cid:8) Based on experimental evidence it is estimated that relative
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`changes in error rate of less than  (cid:28) for a single speaker and less than (cid:28) for the  speaker
`
`average are statistically insigni(cid:21)cant(cid:8)
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`Baseline Experiments(cid:3)
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`Baseline results were obtained with SIGPSTD(cid:7) doing training
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`and testing in the same environment(cid:16)Table (cid:17)(cid:8) Training in the clean environment and de(cid:6)
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`coding in the noisy one or vice(cid:6)versa did produce average error rates of more than (cid:26) no
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`precise error counts are given because the many insertion errors made counting by the above
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`principles barely possible(cid:8)
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`Results with Noise Immunity derived from Signal Processing only(cid:3) The spectral
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`subtraction threshold in NOISIM is chosen such that a dB dynamic range (cid:16)DYN in (cid:16) (cid:17)(cid:17)
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`is available after spectral subtraction in all environments(cid:8) The results obtained with this
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`implementation are given in Table (cid:8)
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` Mainframe CPU minutes can not readily be interpreted as percentage of real(cid:15)time performance on the
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`portable Tangora system(cid:6) because of di(cid:16)erences in implementation(cid:4)
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` 
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`IPR No. 2017-00627
`Apple Inc. v. Andrea Electronics Inc. - Ex. 1033, p. 15
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`

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`ENVIRONMENT
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`CLEAN NOISY
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`MAP
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`PDM
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`NBD
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`MAM
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`ABK
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`AVG (cid:16) (cid:31)errors (cid:17)
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` (cid:8)
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` (cid:8)
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`AVG (cid:16) (cid:28)errors (cid:17)
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` (cid:8) (cid:28)
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`(cid:8) (cid:28)
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`AVG (cid:16) decoding time (cid:17)
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`  min(cid:8)
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`  min(cid:8)
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`Table (cid:18) Performance of SIGPSTD in a constant environment(cid:8)
`
`TRAIN(cid:27)CLEAN
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`TRAIN(cid:27)NOISY
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`SPK
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`TEST(cid:27)CL TEST(cid:27)NO TEST