SPEECH RECOGNITION CIRCUIT USING PARALLEL PROCESSORS
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`The present invention generally relates to a speech recognition circuit which uses
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`parallel processors for processing the input speech data in parallel.
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`Conventional large vocabulary speech recognition can be divided into two processes:
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`front end processing to generate processed speech parameters such as feature vectors,
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`followed by a search process which attempts to find the most likely set of words spoken
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`from a given vocabulary (lexicon).
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`The fi'ont end processing generally represents no problem for current processing
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`systems. However, for large vocabulary, Speaker independent speech recognition, it is
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`the search process that presents the biggest challenge. An article by Deshmukh et a1
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`entitled "Hierarchical Search for Large-Vocabulary Conversational Speech
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`Recognition" HERE Signal Processing Magazine, September 1999, pages 84 to 107),
`the content of which is hereby incorporated by reference, discusses the general concepts
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`of large vocabulary speech recognition. As discussed in this paper, one algorithm for
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`perfonning the search is the Viterbi algorithm. The Viterbi algorithm is a parallel or
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`breadth first search through a transition network of states of Hidden Markov Models.
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`An acoustic model for words in a lexicon are represented as statesiof Hidden Markov
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`Models. These states represent phones or n phones in a phone model of the words. The
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`search requires the evaluation ofpossible word matches. It is known that such a search
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`is computationally. intensive.
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`In order to speed up the processing performed during such a search in a speech
`recognition system, parallel processing has been explored. In an article by
`M K Ravishankar entitled "Parallel Implementation of Fast Beam Search for Speaker—
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`Independent Continuous Speech Recognition" (Indian Institute of Science, Bangalor,
`India, July 16, 1993) a multi-threaded implementation of a fast beam search algorithm is
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`disclosed. The multi-threading implementation requires a significant amount of
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`communication and synchronization among threads. In an MSC project report by
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`2
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`R Dujari entitled "Parallel Viterbi Search Algorithm for Speech Recognition" (MIT,
`February 1992) the parallel processing of input speech parameters is disclosed in which
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`a lexical network is split statically among processors.
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`It is an object of the present invention to provide an improved circuit which can perform
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`parallel processing of speech parameters.
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`In accordance with a first embodiment of the present invention, a speech recognition
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`circuit comprises an input port such as input buffer for receiving parameterized speech
`data such as feature vectors. A lexical memory arrangement is provided Which contains
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`lexicon data for word recognition. The lexical data comprises a plurality of lexical tree
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`data structures representing a plurality of lexical trees. Each lexical tree data structure
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`comprises a model ofwords having common prefix components and an initial
`component which is unique as an initial component for lexical trees. A plurality of
`lexical tree processors are connected in parallel to the input port and perform parallel
`lexical tree processing for word recognition by accessing the lexical data in’ the lexical
`memory arrangement A results memory arrangement is connected to the lexical tree
`processors for storing processing results from the lexical tree processors and lexical tree
`identifiers to identify lexical trees to be processed by the lexical tree processors. A
`controller controls the lexical tree processors to process lexical trees identified in the
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`results memory arrangement by performing parallel processing of a plurality of lexical
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`tree data structures.
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`Thus in accordance with this embodiment ofthe present invention, the processing in
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`order to perform word recognition is distributed across the processors by controlling the
`processors to perform processing on different lexical trees. The controller controls the
`processor by the processes to provide for efficient process management by distributing
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`’lexical processing to appropriate processors.
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`The lexical tree data structure can comprise a phone model of words, wherein the
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`components comprise phones. For reduced storage, the lexical tree data structure can
`comprise a mono phone lexical tree. The mono phone lexical tree can be used to
`generate context dependent phone models dynamically. This enables the use of context ‘
`dependent phone models for matching and hence increased accuracy whilst not
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`increasing memory requirements. Alternatively, the lexical tree data structure can
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`comprise context dependent phone models.
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`The processing performed by each processor in one embodiment comprises the
`comparison of the speech parameters with the lexical data, e.g. phone models or data
`derived from the lexical data (e.g. dynamically generated context dependent phone
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`models) to identify words as a word recognition event and to send information
`identifying the identified words to the results memory as the processing results. In this
`embodiment a language model processor arrangement can be provided for providing a
`language model output for modifying the processing results at a word recognition event
`by a lexical tree processor. The modification can either take place at each lexical tree
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`processor, or at the language model processing arrangement.
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`In one embodiment each lexical tree processor deterrnirnes an output score for words in
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`the processing results at word recognition events. Thus in this embodiment the
`language model processing arrangement can modify the score using a score for a
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`language model for n preceding words, where n isan integer.
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`In one embodiment the controller instructs a lexical tree processor to process a lexical
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`tree by passing a lexical tree identifier for the lexical tree and history data for a
`recognition path associated with the lexical tree from the results memory. The history
`data preferably includes an accumulated score for the recognition path. This enables a
`score to be determined based on the score for the recognition path to accumulate a new
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`score during recognition carried out using the lexical tree data structure. The scores can
`be output in the processing results to the results memory during the processing ofthe
`speech. parameters so that the scores can be used for pruning.
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`In one embodiment of the present invention, each lexical tree processor operates on
`more than one lexical tree at the same time, e.g; two lexical trees represented by two
`different lexical tree data structures, or two lexical trees represented by the same data
`structure but displaced in time (which canebe termed to instances ofthe same lexical
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`tree).
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`At word recognition events, the controller determines new lexical tree identifiers for
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`storing in the results memory for words identified in the results memory for respective
`word events. In order to reduce the processing, the controller can prune the new lexical
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`tree identifiers to reduce the number oflexical trees which are required to be processed.
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`This pruning can be achieved using context dependant n phones to reduce the number of
`possible next phones. The number can be further reduced by using a language model
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`look ahead technique.
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`In one embodiment of the present invention, the lexical tree processors are arranged in
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`groups or clusters. The lexical memory arrangement comprises a plurality of partial
`lexical memories. Each partial lexical memory is connected to one of the groups of
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`lexical tree processors and contains part of the lexical data. Thus a group of lexical tree
`processors and apartial lexical memory form a cluster. Each lexical tree processor is
`operative to process the speech parameters using a partial lexical memory and the
`controller controls each lexical tree processor to process a lexical tree corresponding to
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`partial lexical data in a corresponding partial lexical memory.
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`In another embodiment of the present invention the lexical memory arrangement
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`comprises a plurality of partial lexical memories. Each partial lexical memory being
`connected to one of the lexical tree processors and containing part of the lexical data.
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`Each lexical tree processor processes the speech parameters using a corresponding
`partial lexical memory and the controller is operative to control each lexical tree
`processor to process a lexical tree corresponding to partial lexical data in a
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`corresponding partial lexical memory.
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`In one embodiment of the present invention the lexical memory arrangement stores the
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`lexical tree data structures as Hidden Markov Models and the lexical tree processors are
`operative to perform the Viterbi search algorithm using each respective lexical tree data
`structure. Thus in this way, this embodiment ofthe present invention provides a
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`parallel Viterbi lexical tree search process for speech recognition.
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`The first aspect ofthe present invention is a special purpose circuit built for performing
`the speech recognition search process in which there are a plurality of processors for
`performing parallel lexical tree processing on individual lexical tree processors.
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`In another aspect of the present invention a speech recognition circuit comprises an
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`input port such as an input buffer for receiving parameterized Speech data such as
`feature vectors. A plurality of lexical memories are provided which contain in
`combination complete lexical data for word recognition. Each lexical memory contains
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`part ofthe complete lexical data. A plurality of processors are provided connected in
`parallel to the input port for processing the speech parameters in parallel. The
`processors are arranged in groups in which each group is connected to a corresponding
`lexical memory to form a cluster. A controller controls each processor to process the
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`speech parameters using partial lexical data read from a corresponding lexical memory.
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`The results of processing the speech parameters are output from the processors as
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`recognition data.
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`Thus this aspect ofthe present invention provides a circuit in which speech recognition
`processing is performed in parallel by groups of processors operating in parallel in
`which each group accesses a common memory of lexical data. This aspect of the
`present invention provides the advantage of parallel processing of speech parameters
`and benefits from a limited segmentation of the lexical data. By providing a plurality of
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`processors in a group with a common memory, flexibility in the processing is provided
`without being bandwidth limited by the interface to the memory that would occur if
`only a single memory were used for all processors. The arrangement is more flexible
`than the parallel processing arrangement in which each processor only has access to its
`own iocal memory and requires fewer memory interfaces (i.e. chip pins). Each
`processor Within a group can access the same lexical data as any other processor in the
`group. Thecontroller can thus control the parallel processing of input speech
`parameters in a more flexible manner. For example, it allows more than one processor
`to process input speech parameters using the same lexical data in a lexical memory.
`This is because the lexical data is segmented into domains which are accessible by
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`multiple processors.
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`In a preferred embodiment this aspect of the present invention is used in combination
`with the first aspect ofthe present invention. In such an arrangement each processor
`performs lexical tree processing and the lexical data stored in each lexical memory
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`6
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`comprises lexical tree data structures which each comprise a model of words having
`common prefix components and an initial component thatis unique.
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`In preferred embodiments ofthe second aspect ofthe present invention, the preferred
`embodiments ofthe first aspect ofthe present invention are incorporated.
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`Embodiments of the present invention will now be described with reference to the
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`accompanying drawings in which:
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`Figure 1 is a diagram ofa speech data processing circuit for generating parameterized
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`speech data (feature vectors);
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`Figure 2 is a diagram cf a speech recognition circuit in accordance with an embodiment
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`of the present invention;
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`Figures 3a and 3b are schematic diagrams illustrating lexical tree structures;
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`Figure 4 is a flow diagram illustrating the process performed by a lexical tree processor
`to determine a temporary lexical tree score in accordance with an embodiment ofthe
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`present invention;
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`Figure 5 is a flow diagram illustrating the process performed by the lexical tree
`processor for processing the input feature vectors in accordance with an embodiment of,
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`the present invention; and
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`Figure 6 is a flow diagram illustrating the process performed by the controller in
`accordance with an embodiment of the present invention.
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`Figure 1 illustrates a typical circuit for the parameterization of input speech data. In this
`embodiment the parameters generated are speech vectors.
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`A microphone 1 records speech in an analogue form and this is input through an anti—
`aliasing filter 2 to an analogue-to-digital converter 3 which samples the speech at
`48 kHz at 20 bits per sample. The digitized output signal is normalized (4) to generated
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`7
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`a 10 millisecond data frame every 5 milliseconds with 5, milliseconds overlap (5). A
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`pre—emphasis operation 6 is applied to the data followed by a hamming window 7. The
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`data is then fast Fourier transformed (FF’I') using a 512 point fast Fourier transform (8:)
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`before being filtered by filter bank 9 into 12 frequencies. The energy in the data fi'ame
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`5 is. also recorded (13) as an additional feature and together with the 12 frequency
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`outputs of the filter bank 9, 13 feature vectors (10) are thus produced and these are
`output as part ofthe 39 feature vectors 14; First and second derivatives (11 and 12) are
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`taken of the 13 feature vectors 10 to complete the generation of the 39 feature vectors
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`l4.
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`, The arrangement illustrated in Figure 1 is purely given for illustration. The present
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`invention encompasses any means by which speech and data can be parameterized to a
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`suitable form for input to the search process as will be described in more detail
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`hereinafier.
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`Figure 2 is a schematic diagram of a speech recognition circuit in accordance with an
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`embodiment of the present invention for performing the search process. The
`parameterized speech data, which in this embodiment comprise feature vectors, are
`input to a feature vector buffer 20. The feature vector buffer 20 is provided to buffer the
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`incoming feature vectors to allow lexical tree processors 21 to read and process the
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`feature vectors in the buffer 20 via a feature vector bus 24. A plurality k of lexical tree
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`processors 21 are arranged in a respective lexical tree processor cluster 22. Each lexical
`tree processor cluster 22 has an acoustic model memory 23 in which is stored lexical
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`data for use by the lexical tree processors 21 within the lexical tree processor cluster 22.
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`Each lexical tree processor 21 in the lexical tree processor cluster 22 is connected to the
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`acoustic model memory 23 within the lexical tree processor 22. There are N lexical tree
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`processor clusters and thus there are Nk lexical tree processors 21 connected by the
`feature vector bus 247to the feature vector buffer 20. Each lexical tree processor '21 is
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`capable ofprocessing a different lexical tree and thus Nk lexical trees can be processed
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`in parallel. The acoustic model memories 23 store as a whole a complete set of lexical
`data, i.e. lexical tree data structures for use in the lexical tree processing by the lexical
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`tree processors 21. Each acoustic model memory 23 contains part or a segment of the
`lexical tree data. Since lexical tree processors 21 in a lexical tree processor cluster 22
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`access the same acoustic model memory 23, it is possible for more than one lexical tree
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`processor‘21 to process the same lexical data. This provides for some degree of
`flexibility in the controlling ofthe processing by the lexical tree processors 21. Further,
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`the acoustic model memories 23 need not contain only one copy of the lexical data. It is—
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`possible to build in a redundancy in the data to further enhance the flexibility. This
`avoids any bottleneck in the processing due to the search processing focusing on a small
`number of lexical trees.
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`A results memory 25 is provided for storing processing results from the lexical tree
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`processors 21 which are received over the path score and history bus 26. The results
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`memory 25 also stores information on lexical trees to identify which lexical trees are to
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`be processed. A search controller 27 is provided to control the processing performed by
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`the lexical tree processors 21 in dependence upon a program and data stored in program
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`and data memory 28. The search controller reads the path scores and lexical tree
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`identifiers from the results memory and controls the lexical tree processors accordingly.
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`’
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`A language model processor 29 is provided which is connected toeach lexical tree
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`processor 21 by a language model bus 30. The language model processor 29 accesses a
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`language model memory 31 to read language model data for provision to lexical tree
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`processors 21 in response to language model data requests. External control of the
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`language model memory 31 is provided by a word constrains input. The language
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`model processor 29 determines a score for a word occurring following N previous
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`words using N grams. When a lexical tree processor requires a language model score a
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`request is sent to the language, model processor 29 over the language model bus 30
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`identifying the currentword and the N—l previous words. A language model score for
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`the N gram can be returned to the lexical tree processor 21 for the modification of the
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`score at the end of a branch of lexical tree processing. The lexieaLtree processor can
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`modify the score in accordance with the language model and output a score to the
`results memory 25 for a word at the end of a branch (ifthe lexical tree processing. Thus
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`the results memory stores the results as an ordered list of scores for words together with
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`their histories.
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`The results memory 25 stores the following data:
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`1.
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`V Initial lexical tree data. This comprises pointers to an initial set of lexical trees.
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`No history data is associated with the initial set of lexical trees. The initial set of lexical
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`trees is predetermined and stored in the results memory 25 based on the most likely
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`initial phones of anutterance. This initial lexical tree data is required to initialize the
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`search process.
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`2.
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`History data for search results. This comprises a record of a recognition path
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`through the lexical tree recognition process performed by the lexical tree processors 21.
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`The history data includes the current word, the previous N-l words, the current
`accumulated score, the phone history (for use in the determination of likely next lexical
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`trees using cross word context dependent tri—phones), and an identifier or pointer to the
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`lexical tree used for identifying the word.
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`3.
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`Best scores for best paths being processed by each lexical tree processor 21.
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`This information enables the search controller'27 to monitor the processing being
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`performed by lexical tree processors. 21 to determine whether a global pruning strategy
`should be applied in order to reassign processing performed by a lexical tree processor
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`if its best score for its best path is below a threshold or well below the best scores for
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`the paths being processed by other lexical tree processors 21 ,
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`Temporary lexical tree scores. These comprise tree scores which are determined
`4.
`, as temporary scores to prune the next lexical trees to be processed at word ends. The
`temporary lexical tree scores include lexical tree identifiers or pointers to identify the
`next lexical trees to be processed. The scores enable the pruning of this list.
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`5.
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`Pruning threshold. This can be a global threshold value for use in the pruning of
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`the lexical trees globally, or a local threshold value for use by a lexical processor for
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`locally pruning the processing performed by the lexical processor 21.
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`The acoustic model memory 23 stores a Hidden Markov Model for acoustically
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`modelling words as lexical trees. The acoustic model memory 23 stores a plurality of
`lexicaltree data structures. Each lexical tree data structure comprises an n phone model
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`of anumber of words having common prefix phones. The first node of the lexical tree
`(the root) comprises a common In phone to all words in the lexical tree and uniquely
`identifies the lexical tree.
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`10
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`Each lexical tree processor 21 includes on=hoard memory (or local memory) to be used
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`during the lexical tree processing. This working memory has to store all ofthe
`parameters currently working on including current scores for all paths being processed
`within the lexical tree, and previous N—l words for the lexical tree. The local storage of
`the previous N—l words enables the lexical tree processor 21, when a word end is
`reached along a branch ofthe lexical tree, to send a request for the language model
`score for an N gram by sending the identity ofthe N—l previous words together with the
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`word identified at the end of the branch.
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`Figure 3a and 3b schematically illustrate lexical trees which can be processed by the
`lexical tree processor 21 during the recognition ofthe two words HARD ROCK. In
`Figure 3a a previous lexical tree terminated at a branch recognizing the werd YOU and
`terminating with the mono phone uw which is associated with tWo context dependent
`tIi-phones y—uw+k and y-uw+h. Thus the context dependent tri-phone associated with
`the last phone in the lexical tree word model for YOU indicates tWo possible next
`lexical trees, i.e. the lexical trees beginning with the mono phone k and h and having a
`left context phone of uw. As can be see in Figure 3a this word end YOU therefore leads
`to two possible next lexical trees. These two lexical trees are traversed in parallel by
`two different lexical tree processors accumulating scores for matching of input feature
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`‘ vectors into Hidden Markov Models ofthe context dependent tri—phones associated with
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`each node in the tree. When the end of the branch is reached, a word end event is
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`reached and a word is recognized. As can be seen in Figure 3a, in this example since
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`four words are of similar phone length, it is possible for the search strategy based on
`inputting feature vectors in parallel to simultaneously reach a number of possible word
`ends. These possible word ends are sent as processing results to the results memory.
`The results memory stores the accumulated score at the word end together with phone
`history to identify the last phone and its associated context dependent tri-phones. In this,
`example, considering the branch Where theword HARD is recognized, the last phone is
`d which has associated with it two context dependent tri-phones r-d+l and r-d+r. Thus
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`the search controller 27 can identify next possible lexical trees using the next phone in
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`the context dependent tri-phone. In this case, as can be seen in Figure 3b, the next
`possible lexical trees begin with the phones r and l and have associated with them
`context dependent tri-phones d—r-l-ao and d-l+ao respectively. Thus these are the next
`lexical trees that require processing following the end node or word event detected in
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`11
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`the processing lexical tree 2. Figure 3b thus represents the processing thatis required at
`the end ofFigure 3a at the word node for HARD in processing the second lexical tree.
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`As can be seen in Figure 3b, the two lexical trees are processed in parallel by traversing
`through the branches to reach word ends by sequentially entering in feature vectors in
`parallel to lexical tree processors processing the respectiVe lexical trees. When the word
`end is reached, the accumulated score is output to the results memory as described
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`before. In this case since it is the last word, the important context dependent tri-phone
`associated withthe final phone has silence (sil) as the following phone. The final phone
`can in fact have 50 context dependent tri-phones associated with it ifthere are 50
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`possible nextiphones (including silence). Figure 3b only illustrates the relevant one for
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`the end of the utterance.
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`Figure 4 is a flow diagram illustrating the processing performed by a lexical tree
`processor 21 in order to determine a temporary lexical tree score for a lexical tree.
`When a word end is identified by a lexical tree processor, the processing results are sent
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`to the results memory 25. The results memory identifies the last phone of the
`recognized words and thus enables the search controller 27 to identify possible next
`lexical trees using context dependent tri—phones as illustrated in Figure 3b. Although in
`theory if there are 50 phones there are 502 possible lexical trees, due to the use ofthe
`context dependant triphones, only 100-200 lexical trees are likely to be identified as
`possible lexical trees. The search controller needs to further prune out the lexical trees
`which are unlikely to generate likely paths with high scores, In order to do this,
`instructions are sent to the lexical tree processors to determine a temporary lexical tree
`score which can be used to prune out lexical trees from the processing which have a low
`score.
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`The processing by the lexical tree processor to generate the temporary lexical tree score
`is shown in Figure 4 .and for this process, following initialization, a lexical tree
`processor 21 awaits receipt of data for a new lexical tree from the search controller 27
`{step 82); The data comprises a lexical tree pointer on an identifier to lexical tree data
`stored in the acoustic model memory 23, i.e. the lexical tree data structure, previous N—l
`words (for use in language model score determination by the language model processor
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`12
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`29), and the current path score, The previous N-l words include the previously
`recognized word for which a score has been accumulated.
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`The lexical tree processor 21 then attempts to read the next feature vector from the
`feature vector buffer 20 (step S3) and ifthis is not available, an error occurs (step S4).
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`If the feature vector is available in the feature vector buffer 20, the lexical tree processor
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`21 reads the feature vector from the buffer 20 (step SS) and evaluates the state
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`transitions for the first lexical tree node using the acoustic model data in the acoustic
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`model memory 23 (step S6). Since the state transitions for the first node will require
`several feature vectors. to complete, a score is determined for a state transition of the
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`first node in the lexical tree (step S7). The lexical tree processor 21 then sends a request
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`to the language model processor 29 for a language model score. The request includes
`theiprevious N-l words and all ofthe possible words represented by the lexical tree data
`structure in the acoustic model memory 23 (step SS). The language model processor
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`returns scores for each ofthe N grams ending in the words represented by the lexical
`tree. The lexical tree processor thus receives language model scores for the words in the
`lexical tree and picks the highest score (step 59). Alternatively, the language model
`processor 29 can select the highest 11 gram score and return this to the lexical tree
`processor for the determination ofthe temporary lexical tree score. The temporary
`lexical tree score for the lexical tree is then generated using the score determined for the
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`first state transition of the first lexical tree node and the highest language model score
`(step 810). The temporary lexical tree score is then sent by the lexical tree processor 2i
`to the results memory 25 ove.r the bus 26 (step 81 1). In this mode ofprocessing, the
`processor then awaits the next data of the new lexical tree from the search controller
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`(step 82).
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`This process is just one ofthe processes performed by the lexical tree processor. The
`main processing performed by the lexical tree processor is the lexical tree evaluation.
`This process will now be described with reference to the flow diagram ofFigure 5.
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`After the start ofthe process (step 820) a lexical tree processor awaits receipt of data for
`a lexical tree from the search controller (step 321). The data comprises a lexical tree
`pointer to a lexical tree data structure stored in the acoustic model memory 23, the
`previous N-l words in the recognition path, and the current accumulated score.
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`The lexical tree processor then attempts to read the next feature vector from the feature
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`vector buffer 20 (step $22). If this is not available, an error occurs (step 523). If the
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`feature vector is available in the feature vector buffer 20, the feature vector is read from
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`the feature vector buffer 20 by the lexical tree processor 21 (step 824) and the lexical
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`tree processor 21 evaluates the state transitions for each path using the acoustic model
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`data in the acoustic model memory 23 (step 825). Scores for the paths are determined
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`in accordance with the conventional Viterbi search technique and the best score for the
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`best path is sent to the results memory 25 while the path histories are stored locally in
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`the on-board memory ofthe lexical tree processor 21 (step 826). Pruning is applied to
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`the lexical tree by the lexical tree processor to delete paths in order to keep the breadth
`of the search manageable (step 827). The pruning applied locally by the lexical tree
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`processor can beipurely on the basis of a local threshold, which can be provided by the
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`search controller 27, or itcan‘be determined on a relative-basis dependent upon the
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`range of scores for paths being processed within the lexical tree processor 2l . If the
`path has not reached a word end (step 828) the lexical tree processor 21 attempts to read
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`the next feature vector from the feature vector buffer 20 (step 822). If the path reaches
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`a word end (step 828) the score must be modified by the language model score (step
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`829). There are two ways in which this can be done, in this embodiment the lexical tree
`processor 21 sends a request to the language model processor 29 for a language model
`score. The request includes the current word and the N—l previous words. The
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`language model processor thus retumsithe language model score and the language
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`model score is used to modify the current accumulated score at the word end;
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`Alternatively, the lexical tree processor can send the language model processor 29 not
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`just the current word and the N-l previous words, but also the current accumulated
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`score. The language model processor then determines the language model score using
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`the language model memory 31 and modifies theiaccuinulated score using the language
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`model score. The language model processor 29 can then return the modified score to
`the lexical tree processor 21 which can then pass it to the results memory 25, or a
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`connection between the language model processor 29 and the results memory 25 (not
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`shown) can enable the language model processor 29 to send the'score directly to the
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`results memory 25 for the lexical tree processor 21. In this latter case, the language
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`' model processor 29 must also receive a lexical tree pointer to identify the lexical tree for
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`which the score applies.
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`14
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`Assuming in this embodiment that the lexical tree processor calculates the modified
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`score (step 829); the score and history is then sent by the lexical tree processor 21 to the
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`results memory 25 (step S30). The history data sent to the results memory 25 comprises
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`the lexical tree pointer identifying the lexical tree, the modified score at the word end,
`the phone history identifying at least the last phone to allow for context dependent tri-
`phone determination of next lexical trees, and the identity of the word identified in the
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`evaluation process.
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`The lexical tree processor then deletes the path and history data in its on-board memory
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`(step SS 1) and determines ifthere are any paths still lefi to be processed (step S32). If
`so, the lexical tree processor 21 tries to access the next feature vector available in the
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`feature vector buffer 20 (step 822). Ifthere are no paths left to be processed, a message
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`is sent by the lexical tree processor to the search controller 27 to indicate that the lexical
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`tree has been processed (step S33). In this level of processing the lexical tree processor
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`will then await the next data for a lexical tree fi'om the search controller 27.
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`The flow diagram ofFigure 5 illustrates the processing ofa single instance of a lexical
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`' tree. The