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`
`12.7.l N-grams
`
`A simple solution to the problem of estimating word-sequence probabilities is to
`use N-grams (where N is a small number). Here it is assumed that the probability
`of observing any word wk depends only on the identity of the previous N - I words
`(so that each conditional probability depends on a total of N words, including the
`current word). Using N-grams Equation (12.3) can be approximated as follows:
`
`K
`
`P(W) = TT P( wk I Wi, ... , wk-I) ~ TT P( wk I wk-N+1' ... , wk-I).
`
`K
`
`(12.4)
`
`k=l
`k=l
`If N = 1 the model is a unigram and just represents the probability of the word. A
`bigram (N = 2) models the probability of a word given the immediately preceding
`word, while a trigram (N = 3) takes into account the previous two words.
`To illustrate the use of N-grams to estimate word-sequence probabilities,
`consider the phrase "ten pots fell over". For a unigram model, the probability of the
`sequence is obtained simply by multiplying together the word probabilities:
`
`P(ten pots fell over)~ P(ten) P(pots) P(fell) P( over).
`
`(12.5)
`
`In the case of a bigram model the probability of the sequence is estimated as:
`P(ten pots fell over):::::: P(ten I START) P(pots I ten)
`P( fell I pots) P( over I fell),
`
`(12.6)
`
`where START is used to indicate the beginning of the sequence. For a trigram
`model, the probability estimate becomes:
`P(ten pots fell over):::::: P(ten I START) P(pots I START ten)
`P( fell I ten pots) P( over I pots fell).
`
`(12.7)
`
`In principle, N-grams can be estimated using simple frequency counts from
`training data to obtain maximum-likelihood estimates for the required probabilities.
`Considering the bigram (wk_ 1, wk) and the trigram (wk_ 2, wk_ 1, wk), the conditional
`probabilities P( wk I wk_ 1) and P( wk I wk_ 2, wk_ 1) could be estimated thus:
`lw
`P(w lw )= C(wk-1,wk)
`C (
`)
`k
`k-2 '
`k-1
`wk-I
`
`P(w
`
`'
`
`k
`
`w
`k-1
`
`)= C(wk-2,wk-l'wk)
`C (
`wk-2' wk-I
`
`)
`
`'
`
`(12.8)
`
`where the notation C(x) is used to represent the count of number of examples of x.
`For the examples of the bi gram ( fell over) and the trigram (pots fell over) we have:
`I~ ll) C (fell over)
`I
`P .. (
`p ... (
`c. ll) C (pots fell over)
`over pots 1e =
`over 1e = -----,
`C (fell)
`C (fell over)
`
`(12.9)
`
`12.7.2 Perplexity and evaluating language models
`
`The task of the language model can be viewed as one of predicting words in a
`sequence, and a good language model will be one that provides a good predictor of
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`provided that the sample size is large in relation to the number of possible
`outcomes. Unfortunately, in the case of N-gram modelling there are a vast number
`of possible outcomes. A vocabulary of V words provides V 2 potential bigrams and
`v3 potential trigrams. For a 20,000-word vocabulary there are thus 400 million
`possible word bigrams and eight million million possible trigrams! It would be
`completely impracticable
`to provide sufficient speech data to determine the
`language-model probabilities, and instead very large text corpora are used. Even
`so, while typical text corpora may contain over 100 million words, most of the
`possible bigrams and the vast majority of possible trigrams will not occur at all in
`the training data and many others will only appear once or twice.
`Data sparsity is a much greater problem for the language model than for the
`acoustic model, due to the much larger size of the inventory of basic units (words
`rather than phones). As a consequence of this severe data-sparsity problem, it is not
`possible to rely only on simple frequency counts for estimating language-model
`probabilities, as many of the bigram and trigram probabilities would be set to zero
`(so that it would then be impossible to recognize any word sequence containing one
`of these combinations) and many other probabilities would be poorly estimated.
`The successful use of N-grarns for L VCSR is dependent upon the use of smoothing
`techniques for obtaining accurate, robust (non-zero) probability estimates for all
`the possible N-grams that can occur for a given vocabulary. Zero probabilities and
`low non-zero probabilities
`are adjusted upwards, and high probabilities are
`reduced. Thus the overall effect is to make the probability distributions more
`uniform, and hence 'smoother'. Some different aspects of smoothing algorithms are
`described briefly in the following sections.
`
`12.7.4 Discounting
`
`such as bigrams or trigrams, the sum of the
`'events',
`For any set of possible
`probabilities for all the possibilities must be equal to one. When only a subset of
`the possible events occurs in the training data, the sum of the probabilities of all the
`observed events must therefore be less than one. This effect can be achieved by
`reducing the observed
`frequency counts. The process is generally known as
`discounting, and is often described in terms of 'freeing' probability mass from the
`observed events which can then be redistributed among the unseen events.
`Several different methods have been used to achieve discounting, and these
`methods differ in the way in which the reduced probabilities are calculated. Full
`coverage of discounting methods is outside the scope of this book but, for example,
`one simple but effective technique is absolute discounting. Here some small fixed
`amount (between zero and one) is subtracted
`from each frequency count ( the
`numerators in Equation (12.8) or in the examples shown in Equation (12.9)). Thus
`the probability of every observed event is reduced, but the effect decreases as the
`observed frequency count increases (when the maximum-likelihood estimate should
`be more reliable). However, the same discount value may not be optimum for the
`full range of frequency counts. In particular, there is some evidence that absolute
`discounting imposes too great a reduction
`in the probability of events that occur
`only once or twice. A variant of the technique overcomes this problem by having
`separate discount values for these rare events.
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`12.7.5 Backing off in language modelling
`
`Probability mass which has been made available as a result of discounting can be
`allocated to the unseen events. In estimating the probabilities of the unseen events,
`it is desirable to make use of any information
`that is available about the relative
`probabilities of the different events. In the case of N-grams, useful information may
`be available in the form of the probability according to a more general distribution.
`Thus it is possible to formulate a recursive procedure of 'backing off': if a trigram
`is not observed, the model backs off to the relevant bigram probability, and if the
`bigram is not available, it backs off further to the unigram probability. For any
`words which do not occur in the training texts at all, it is possible to back off to a
`uniform distribution whereby all words are assumed to be equally likely.
`Backing off can be illustrated by considering
`the task of estimating the
`conditional trigram probabilities P( wk I wk_ 2 , wk_ 1) for word wk in the context of
`the sequence of preceding words (wk_ 2 , wk_ 1). We will assume that some
`appropriate discounting method has been used
`to assign probabilities to all
`observed
`trigrams,
`and
`these
`probability
`estimates will be denoted
`P(wk I wk_
`Thus, using Ps(wk I wk- 2
`, wk_ 1).
`, wk- 1
`to denote an estimate for the
`probability of word wk given the sequence of preceding words (wk-2, wk_,), a
`backing-off scheme for obtaining this probability estimate is as follows:
`
`)
`
`2
`
`5
`
`if C(wk_2 , wk_1, wk)> 0
`) otherwise.
`(12.11)
`
`) = {P(wk I wk_2, wk_1)
`I
`p (
`s wk wk_z,wk-l
`(wklwk_ 1
`B(wk_2 ,wk_1)P
`Ps(wk I wk- 1) is the estimated bigram probability of wk given preceding word wk-I·
`B( wk- 2, wk- 1) is a normalizing constant for trigram context ( wk- 2, wk- 1), and scales
`the bigram probabilities so that the sum of the Ps( wk I wk_ 2, wk- 1) terms is equal to
`I when computed over all possible words wk. The sum of all the probabilities that
`are calculated by backing off must be equal to the total probability mass that has
`been freed from discounting the relevant trigram context. The normalizing constant
`is therefore chosen to be equal to the appropriate fraction of this freed probability.
`Backing off from bigram
`to unigram and
`from unigram
`to unifonn
`distributions can be accomplished in an analogous manner.
`By setting the count threshold to zero, backing off only occurs for N-grams
`with no examples at all. However, as observing just one or two examples is unlikely
`to provide a reliable probability estimate, it can be beneficial to apply a higher
`threshold and so disregard those N-grams that occur just a few times in the training
`data. In this way only robustly estimated N-grams should be retained, which has the
`added benefit of a substantial saving in the memory needed for the language model.
`
`12.7.6 Interpolation of language models
`
`Backing off involves choosing between a specific and a more general distribution.
`An alternative is to compute a weighted average of different probability estimates,
`obtained for contexts that can range from very specific to very general. The idea is
`to improve the accuracy and robustness of a context-specific probability estimate
`by combining it with more general estimates for which more data are available. One
`option involves taking a linear combination of different probability estimates. For
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`example, a trigram probability could be estimated by linear interpolation between
`relative frequencies of the relevant trigrams, bigrams and unigrams, thus:
`
`I
`Ps (wk Wk-2, Wk-I
`
`A C(wk) (1212 )
`A C(wk-1,wk)
`) =A C(wk-2,wk-t,Wk)
`,
`•
`3 C(
`) + 2 C(
`) + I K
`Wk-2, Wk-1
`Wk-I
`
`where K is the number of different words, and the sum of the non-negative weights
`A.t + J 2 + ,.1,3 = 1 . The values for the weights need to be chosen to make the best
`compromise between specificity and ability to generalize to new data. The training
`data are therefore divided into two parts. The first (larger) part of the data is used to
`derive the frequency counts, which are then used when finding the optimum values
`of the weights in order to maximize the probability for the second part of the data.
`Because the parameters that are estimated will tend to depend on how the data are
`partitioned, often alternative sets of parameters are estimated for several different
`ways of splitting the data, and then the individual estimates are combined. This
`smoothing method is often called deleted interpolation.
`For simplicity, interpolation has been introduced using probability estimates
`obtained from simple frequency counts. However, smoothing by interpolation can
`also be applied to other probability estimates, including N-gram probabilities that
`have been obtained by, for example, the absolute discounting technique mentioned
`in Section 12.7.4. Interpolation schemes can also be used with probabilities from
`other types of language model, such as those discussed in Section 12. 7. 8 below.
`
`12.7.7 Choice of more general distribution for smoothing
`
`In the previous sections we have described ways of smoothing N-grams using
`lower-order N-grams. The lower-order distribution
`is usually defined in a way
`which is exactly analagous to the definition for the higher-order distribution that is
`being smoothed. There is however an alternative method which may give more
`reliable estimates of the lower-order probabilities. This method is best explained by
`describing an example. Consider a word, such as "Francisco", that almost always
`occurs following just one other word ("San"). If the training text contains several
`examples of "San Francisco", both the bigram probability P(Francisco!San) and the
`unigram probability P(Francisco) will be high. Thus if we then use a discounting
`method such as absolute discounting
`to derive the probability of "Francisco"
`occurring after some other bigram history, this probability will also tend to be fairly
`high. However, intuitively it seems more plausible that, if the only information we
`have implies that "Francisco" always follows "San", the probability of "Francisco"
`following any other word should be low.
`It is possible to define the unigram probability used for smoothing not in
`terms of number of examples of a word but rather in terms of the number of
`different contexts in which the word occurs. Chen and Goodman ( 1999) have
`~roposed using this approach
`together with smoothing by interpolation, and
`mcorporating a variant of absolute discounting
`that uses three separate discounts
`(one for N-grams occurring just once, one for N-grams occurring twice, and a third
`fo~ all other N-gram counts). In comparative experiments, they demonstrated that
`this method performed consistently better
`(in terms of both perplexity and
`recognition performance) than a wide range of other smoothing techniques.
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`12,8 DECODING
`
`The recognition task is to find the most probable sequence of words W, as given by
`Equation ( 12.1 ). As the vocabulary size becomes larger, the number of different
`possible word sequences soon becomes prohibitive and any practical system needs
`an efficient means of searching to find the most probable sequence. The component
`of the system that performs this search operation is often referred to as the decoder.
`Although recognition using whole-word HMMs is not practicable with a large
`vocabulary, the same principles can be applied to units of any size. In the previous
`sections we have explained how statistical models can be used at different levels.
`One level captures the relationship between phone-size units and their acoustic
`realization, then there are the different phone sequences that can make up a word,
`and finally there are the probabilities of different word sequences. Thus a language
`can be modelled as a network of states representing linguistic structure at many
`levels. At the lowest level a small network of states represents a triphone or similar
`unit. At the next level a network of triphones fonns a state to represent a word. A
`complete sentence can then be generated by a network of word states, where the
`connections between the states correspond to the language-model probabilities. The
`decoding task is to find the best path through this multiple-level network, and the
`recognized word sequence is given by the best path at the highest level.
`In order to apply the principles of DP using the Viterbi algorithm to a
`multiple-level network, probabilities need to be evaluated for all valid partial paths
`at all levels, with no decisions being reached until all earlier parts of a path enter
`the same highest-level state at the same time, thus delaying decisions until all
`relevant evidence has been used. The use of delayed decisions is a fundamental
`principle of this HMM approach, as it enables the knowledge and constraints at all
`levels to be employed simultaneously
`to find the best explanation of the data. In
`practice, even at any one point in time, in a large-vocabulary system there are so
`many possibilities (both at the word and at the sub-word level) that it is impossible
`to evaluate all of them. However, the language model provides strong constraints
`that act to make many of the possibilities extremely unlikely, and it is necessary to
`find an efficient way of applying these constraints within the decoding.
`In a first-order HMM the probability of a transition from any one state to any
`other state depends only on the identities of the source and destination states. This
`model cannot accommodate a trigram ( or higher-order) language model, because
`here the word transition probability depends on more than just the immediately
`preceding state. In order to use such a language model, some way of dealing with
`the higher-order dependencies needs to be found. An additional complication arises
`when using cross-word triphones because the identity of a word-final triphone, and
`hence the word probability, depends on the identity of the following word.
`The issues associated with large-vocabulary decoding have been addressed in
`various different ways. Three main types of method are briefly described below.
`
`12.8.1 Efficient one-pass Viterbi decoding for large vocabularies
`
`triphone models, the state network for a
`In_ order to accommodate cross-word
`Viterbi search needs to include multiple entries for each word to cover all possible
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`Speech Synthesis and Recognition
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`Journal (WSJ), and included both a 5,000-word recognition test and a 20,000-word
`test. More recently, the test set was expanded to include a range of North American
`business (NAB) news and restrictions on training for the acoustic and language
`models were removed. The recognition vocabulary became essentially unlimited
`and performance was generally evaluated using systems designed for recognizing
`about 64,000 words. WSJ and NAB evaluations were conducted in the period
`between 1992 and 1995. Over the years the complexity and variety of the tests
`increased, but recognition performance still improved each year (see Figure 12.6).
`Later ARP A evaluations moved away from read speech, and concentrated on
`transcribing
`'found' speech from broadcast news (BN) shows on radio and
`television. Such data provided a lot of variation in speech quality, microphones and
`background noise, as well as non-native speech and periods containing only non(cid:173)
`speech data such as sound effects and music. For the first BN evaluation in 1996,
`performance dropped considerably from that which had been obtained in the
`previous evaluations using read speech. However, researchers soon found ways of
`coping with the greater variety of material, and by the final BN evaluations in 1998
`and 1999 typical error rates had roughly halved from those obtained in 1996.
`ARP A have also sponsored a research programme on the recognition of
`conversational speech, including the collection of two different corpora. Data for
`one corpus, known as Switchboard (SWB), first became available in 1992. The
`data comprised telephone conversations between two strangers about some topic
`that had been specified in advance. A few years later, in 1995, collection of the
`Callhome (CH) corpus was initiated to provide more natural conversational speech
`by recording telephone conversations between family members. The CH set
`includes a number of different languages. Conversational speech is rather different
`in nature to either read speech or television and radio broadcasts. Conversations
`tend to include many disfluencies, false starts and so on, as well as a lot of
`phonological reduction and special use of prosody. There is also tum-taking
`behaviour and reliance on shared knowledge
`to assist in the communication,
`especially when the participants know each other very well ( as in the CH data). As
`a result, errors are much higher than for the other tasks (see Figure 12.6), but again
`substantial progress has been made in improving performance on the conversational
`recognition tasks. As of 2001, these evaluations are still continuing.
`The ARP A initiative has been very influential in the development of large(cid:173)
`vocabulary recognizers, both by providing a focus for refining the techniques to
`cope with increasingly difficult problems and also by making available huge
`quantities of speech data for training. Over the years, the result has been a
`progressive improvement in performance on increasingly difficult tasks, as can be
`seen in Figure 12.6. There are now several research systems which give impressive
`performance on substantial transcription tasks. However, to perform at their best
`these systems generally require both a lot of memory ( with typically several million
`parameters in both the acoustic and language models), and a lot of processing
`power ( often operating at a few hundred times real time). In the last two BN
`evaluations, the effect of processing demands was tested by introducing a category
`for systems that operated at no slower than 10 times real time (shown as BN(IOx)
`in Figure 12.6). Enforcing this constraint
`led to around 15% more errors than if
`there were no processing restrictions. We will consider performance in relation to
`the requirements of different applications in Chapter 15.
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`12.10 SPEECH UNDERSTANDING
`
`In order to understand spoken language, it is necessary not only to recognize the
`words, but also to determine the linguistic structure, including both syntactic and
`semantic aspects. Given a word sequence, the field of computational linguistics
`provides techniques for deriving a representation of the linguistic structure.
`Traditionally these techniques
`involve a complex linguistic analysis based on
`qualitative models that are usually stated in the form of a grammar that is specified
`manually by human experts, although in recent years data-driven statistical methods
`have also been used. Even so, any detailed
`linguistic analysis will involve
`modelling long-range effects that may operate over the entire span of an utterance.
`It is very difficult to incorporate such a model into a speech recognition search.
`Due to the difficulties involved in attempting to incorporate detailed linguistic
`analysis into the recognition search, speech understanding systems generally treat
`these tasks as separate components: an ASR system of the type described earlier in
`this chapter, and a second component that uses artificial-intelligence techniques to
`perform linguistic analysis
`in order to 'understand'
`the recognizer output. To
`reduce the problems caused by errors made at the recognition stage, the output of
`the recognizer should include alternatives, which can be provided in the form of a
`word lattice or an N-best list. The final recognized output is typically taken to be
`the highest-scoring alternative that is also allowed by the linguistic model.
`The process of determining the linguistic structure of a sentence is known as
`parsing. Syntactic structure is usually expressed in terms of a formal grammar, and
`there are a variety of grammar formalisms and associated methods for syntactic
`parsing. However, traditional parsers aim to recover complete, exact parses. This
`goal will often not be achievable for spoken language, which tends to contain
`grammatical errors as well as hesitations, false starts and so on. The problem is
`made even worse when dealing with the output of a speech recognizer, which may
`misrecognize short function words even when overall recognition performance is
`very good. It can therefore be useful to adopt partial parsing techniques, whereby
`only segments of a complete word sequence are parsed (for example, noun phrases
`might be identified). Other useful tools include part-of-speech taggers, which aim
`to disambiguate parts of speech ( e.g. the word "green" acts as an adjective in the
`phrase "green coat" but as a noun in "village green"), but without performing a
`parsing operation. Some taggers are rule-based, but there are also some very
`successful taggers that are based on HMMs, with the HMM states representing tags
`(or sequences of tags). Transition probabilities are probabilities of tag(s) given
`previous tag(s) and emission probabilities are probabilities of words given tags.
`Partial parsing and part-of-speech
`tagging enable useful linguistic information to be
`extracted from spoken input without requiring a comprehensive linguistic analysis.
`General semantic analysis for the derivation of meaning representations is a
`complex aspect of computational
`linguistics. However, many speech-understanding
`systems have used a simpler approach
`that is more application dependent. A
`popular technique uses templates, or 'frames', where each frame is associated with
`an action to be taken by the system. A frame has 'slots' for different items of
`relevant infonnation. In a flight enquiry system for example, one frame could
`represent a request for flight information. This frame could include slots for the
`type of infonnation ( e.g. flights, fares) and for any constraints ( e.g. date, price).
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`CHAPTER 12 SUMMARY
`
`•
`
`• Some large-vocabulary recognition tasks may require accurate transcription of
`the words that have been said, while others will need understanding of the
`semantic content (but not necessarily accurate recognition of every word).
`• For speech transcription the task is to find the most likely sequence of words,
`where the probability for any one sequence is given by the product of acoustic(cid:173)
`model and language-model probabilities.
`• The principles of continuous speech recognition using HMMs can be applied
`to large vocabularies, but with special techniques to deal with the large number
`of different words that need to be recognized.
`It is not practical or useful to train a separate model for every word, and
`instead sub-word models are used. Typically phone-size units are chosen, with
`the pronunciation of each word being provided by a dictionary.
`• Triphone models represent each phone in the context of its left and right
`neighbours. The large number of possible triphones is such that many will not
`occur in any given set of training data. Probabilities for these triphones can be
`estimated by 'backing of~ or interpolating with biphones ( dependent on only
`the left or the right context) or even context-independent monophones.
`• Another option, which allows greater context specificity to be achieved, is to
`group ('cluster') similar triphones together and share ('tie') their parameters. A
`phonetic decision tree can be used to find the best way to cluster the triphones
`based on questions about phonetic context. The idea is to optimize the fit to the
`data while also having sufficient data available to train each tied state.
`• An embedded training procedure
`is used, typically starting by estimating
`parameters for very general monophone models for which a lot of data are
`available. These models are used to initialize triphone models. The triphones
`are trained and similar states are then tied together. Multiple-component
`mixture distributions are introduced at the final stage.
`• The purpose of the language model is to incorporate language constraints,
`expressed as probabilities
`for different word sequences. The perplexity, or
`average branching factor, provides a measure of how good the language model
`is at predicting the next word given the words that have been seen so far.
`• N-grams model the probability of a word depending on just the immediately
`preceding N- 1 words, where typically N = 2 ('bigrams') or N = 3 ('trigrams').
`• The large number of different possible words is such that data sparsity is a
`massive problem for language modelling, and special techniques are needed to
`estimate probabilities for N-grams that do not occur in the training data.
`• Probabilities for N-grams that occur in the training text can be estimated from
`frequency counts, but some probability must be 'freed' and made available for
`those N-grams that do not occur. Probabilities for these unseen N-grams can
`then be estimated by backing off or interpolating with more general models.
`• The principles of HMM recognition extend to large vocabularies, with a
`multiple-level structure in which phones are represented as networks of states,
`words as networks of phones, and sentences as networks of words. In practice
`the decoding task is not straightforward due to the very large size of the search
`space, especially if cross-word
`triphones are used. Special treatment is also
`required for language models whose probabilities depend on more than the
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`immediately preceding word (i.e. for models more complex than bigrams). The
`one-pass Viterbi search can be extended to operate with cross-word triphones
`and with trigram language models, but the search space becomes very large
`and is usually organized as a tree. Efficient pruning is essential.
`• An alternative search strategy uses multiple passes. The first pass identifies a
`restricted set of possibilities, which are typically organized as an N-best list, a
`word lattice or a word graph. Later passes select between these possibilities.
`Another option is to use a depth-first search.
`• Automatic speech understanding needs
`further processing of the speech
`recognizer output to analyse the meaning, which may involve syntactic and
`semantic analysis. To reduce the impact of recognition errors, it is usual to
`start with an N-best list or word lattice. Partial parsing techniques can be used
`for syntactic analysis to deal with the fact that the spoken input may be
`impossible to parse completely because parts do not fit the grammar, due to
`grammatical errors, hesitations and so on.
`• Meaning is often represented using templates, which will be specific to the
`application and have 'slots' that are filled by means of a linguistic analysis.
`In spoken dialogue systems, a dialogue manager
`is used to control the
`interaction with the user and ensure that all necessary information is obtained.
`• ARP A has been
`influential
`in promoting progress
`in large vocabulary
`recognition and understanding, by sponsoring the collection of large databases
`and running series of competitive evaluations. Error rates of less than 10%
`have been achieved for transcribing unlimited-vocabulary read speech and for
`understanding spoken dialogue queries. Recognition of more casually spoken
`spontaneous speech is still problematic.
`
`•
`
`CHAPTER 12 EXERCISES
`
`E12.1 Explain the different requirements and problems in speech transcription and
`speech understanding.
`E12.2 What are the special issues for the design of the acoustic model, the
`language model and the search component when a recognizer needs to cope
`with a large vocabulary size?
`E12.3 Give examples of how acoustic/phonetic knowledge can be used when
`choosing an acoustic model set for a large-vocabulary recognition system.
`El 2.4 When estimating trigram language-model probabilities based on counts in
`some training text, explain how probabilities can be estimated for trigrams
`that do not occur in the training data. How does this operation affect the
`probabilities that need to be assigned to trigrams that do occur?
`El2.5 What are the similarities and differences between the concept of 'backing
`off in language modelling and in acoustic modelling?
`El2.6 Explain the relative merits of performing large-vocabulary recognition using
`a one-pass Viterbi search versus using a multiple-pass search strategy.
`El2.7 What measures are required
`understanding system?
`
`the performance of a speech
`
`to evaluate
`
`IPR2023-00037
`Apple EX1016 Page 101
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