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

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CHAPTER 3. HIDDEN MARKOV MODELS 64test is to use pronunciationmodels in an actual system that handles acoustic data, a considerably more involvedtask. In Section 3.6.3 we will describe such a system and how the HMM merging process was brought to bearon it.The full TIMIT dataset consists of 53355 phonetic samples for 6100 words. 16 To keep the tasksomewhat manageable, and to eliminate a large number of words with too few samples to allow meaningfulstructural model induction, we used a subset of this data consisting of words of intermediate frequency.Arbitrarily, we included words occurring between 20 and 100 times in the dataset. This left us with a workingdataset of 206 words, comprising a total of 7861 samples. Of these, 75% for each word (5966 total) weremade available to various training methods, while the remaining 25% (1895 total) were left for evaluation.3.6.2.2 Qualitative evaluationIn preliminary work, while evaluating the possibility of incorporating HMM merging in an ongoingspeech understanding project, Gary Tajchman, a researcher at ICSI with extensive experience in acousticphoneticmodeling, experimented with the algorithm using the TIMIT database. He inspected a large numberof the resulting models for ‘phonetic plausibility’, and found that they generally appeared sound, in that thegenerated structures were close to conventional linguistic wisdom.To get an idea for the kinds of models that HMM merging produces from this data it is usefulto examine an example. Figure 3.13 shows an HMM constructed from 37 samples of the word often. Forcomparison, Figure 3.12 shows the initial HMM constructed from the samples, before merging (but withidentical paths collapsed).Figure 3.14 plots the number of states obtained during on-line (incremental) merging as a functionof the number of incorporated samples. The numbers of states before and after merging at each stage areplotted as adjacent datapoint, giving rise to the spikes in the figure. Merging starts with five incorporatedsamples (17 states). Initially merging occurs with almost every additional sample, but later on most samplesare already parsed by the HMM and require no further merging.The most striking feature of the induced HMM is that both the first and the second syllable of oftencontain a number of alternative pronunciations anchored around the central [f] consonant, which is commonto all variants. This structural property is well mirrored in the two branching sections of the HMM. A numberof the pronunciation for the second syllable share the optional [tcl t] sequence.Notice that each state (except initial and final) has exactly one output symbol. This constraint wasimposed due to the particular task we had in mind for the resulting HMMs. The speech recognition systemfor which these word models are intended implicitly assumes that each HMM state represents a unique phone.Such a restriction can be easily enforced in the algorithm by filtering the merge candidates for pairs of stateswith identical emissions.The single-output constraint does not limit the representational power of the HMMs, since a multioutputstate can always be split into several single-output states. It does, however, affect the structural prior16 This is the union of the ‘training’ and ‘test’ portions in the original TIMIT distribution.
CHAPTER 3. HIDDEN MARKOV MODELS 65ao f tcl tq ao f tclaa f ix nq aa f axaa f ax naxtao f tcl taw f tcl tq ao f enao f ax nao f ah nnenixnihao f tcl tnnStartq aa f ixixnEndao f epi ennnnao f ax nxnnnq ao f axihao f tcl tnixq ao f ihixaa f tcl tq ao f ixtq aa f enao f enao f ixntclq ao fFigure 3.12: Initial HMM constructed from 37 samples of the word often.Probabilities are omitted in this graph. Due to repetitions in the data the HMM has only 23distinct paths.
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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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122Chapter 6Efficient parsing with
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Consider the following problem: sta
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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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