The dissertation of Andreas Stolcke is approved: University of ...

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CHAPTER 3. HIDDEN MARKOV MODELS 723.6.3 Multiple pronunciation word models for speech recognitionAs part of his dissertation research, Wooters (1993) has used HMM merging extensively in thecontext of the Berkeley Restaurant Project (BeRP). BeRP is medium vocabulary, speaker-independent spontaneouscontinuous speech understanding system that functions as a consultant for finding restaurants in thecity of Berkeley, California (Jurafsky et al. 1994a).In this application, the merging algorithm is run on strings of phone labels obtained by Viterbialigningpreviouslyexisting word models to sample speech (using the TIMIT labels as the phone alphabet). Asa result, new word models are obtained, which are then again used for Viterbi alignment, leading to improvedlabelings, etc. This procedure is iterated until no further improvement in the recognition performance (or thelabelings themselves) is observed. The word models are bootstrapped with a list of pronunciations from avariety of databases. The goal of the iteration with repeated alignments and mergings is to tailor the wordmodels to the task-specific data at hand, and to generalize from it where possible.An added complication is that HMMs with discrete outputs are not by themselves applicable toacoustic speech data. Using an approach developed by Bourlard & Morgan (1993), the HMMs are combinedwith acoustic feature densities represented by a multi-layer perceptron (MLP). This neural network mapseach frame of acoustic features into the phone alphabet. From the network outputs, the likelihoods of theHMM states relative to the acoustic emissions can be computed, as required for the Viterbi alignment or otherstandard HMM algorithms.Since these emission probabilities are also subject to change due to changes of the word models,they too have to be reestimated on each iteration. The MLP is bootstrapped with weights obtained by trainingon the pre-labeled TIMIT acoustic data. Figure 3.15 depicts the combined iteration of MLP training, wordmodel merging, and Viterbi alignment. It can be viewed as an instance of a generalized EM algorithm, inwhich emission probabilities (represented by the MLP) and HMM structure and transition probabilities areoptimized separately. The separation is a result of the different technologies used.For the BeRP system, HMM merging made it possible and practical to use multiple pronunciationword models, whereas before it was confined to single pronunciation models. (Note that in this setting, evena very restricted HMM can produce any acoustic emission with non-zero probability, due to the continuousnature of the domain, and because the emission distribution represented by the MLP is inherently nonvanishing.)To assess its effectiveness, the recognition performance of the multiple-pronunciation system wascompared against that of an otherwise identical system in which only one phone sequence per word was used(generated by a commercial text-to-speech system). In this comparison, multiple-pronunciation modeling asderived by HMM merging was found to reduce the word-level error rate from 40.6% to 32.1%. At the sametime, the error rate at the level of semantic interpretations dropped from 43.4% to 34.1%.Further experiments are needed to identify more precisely what aspects of the multiple-pronunciationapproach account for the improvement, i.e., whether other word model building techniques would lead tosignificantly different results in this context. However, the preliminary results do show that HMM merging is
CHAPTER 3. HIDDEN MARKOV MODELS 73TrainingDataProbabilitiesForwardPass w/TIMIT MLPTaskIndependentWordModelsForcedViterbiAlignmentSentenceStringsLabelsTrainMLPForward PassProbabilitiesPronunciationsEstimateWord Models(HMM Merging)Word ModelsForcedViterbiAlignmentSentenceStringsLabelsPronunciationsTrainMLPForward PassProbabilitiesEstimateWord Models(HMM Merging)Word ModelsForcedViterbiAlignmentSentenceStringsLabelsPronunciationsFigure 3.15: Hybrid MLP/HMM training/merging procedure used in the BeRP speech understanding system(Wooters 1993).
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The dissertation of Andreas Stolcke
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Bayesian Learning of Probabilistic
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iAcknowledgmentsLife and work in Be
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iiiContentsList of FiguresList of T
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CONTENTSv4.5.4 Summary and Discussi
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CHAPTER 1. INTRODUCTION 2Instance-b
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CHAPTER 8. FUTURE DIRECTIONS 1828.2
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184BibliographyAHO, ALFRED V., RAVI
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BIBLIOGRAPHY 186DAGAN, IDO, FERNAND
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BIBLIOGRAPHY 188——, & ——. 1
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BIBLIOGRAPHY 190QUINLAN, J. ROSS, &
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BIBLIOGRAPHY 192WALLACE, C. S., & P
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