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

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CHAPTER 3. HIDDEN MARKOV MODELS 62b.K6 0£ 0181.001.00a0.20b0.33ab0.60a0.331.00a0.17 0.830.331.00Start0.67 1.00ab1.00 0.20ba1.00b1.00End0.330.330.331.001.00a0.33a1.00b.K6 0£ 180.250.250.250.25Start1.00 0.75 0.750.750.75a b abEnd.K6 1£ 00.250.370.251.000.75 0.37Start a b EndFigure 3.11: Case study II: Generalization depending on global prior weight.
CHAPTER 3. HIDDEN MARKOV MODELS 633.6.1.5 DiscussionIt is tempting to try to find a pattern in the performance of the Baum-Welch estimator in terms ofparameters such as model size and sample size and type (although this would go beyond the intended scope ofthis study). Regarding model size, one would expect the smaller minimal models to produce better coverageof the target language, and a tendency to overgeneralize, since too few states are available to produce a closefit to the data. This is indeed observable in the plots in the bottom rows of Figures 3.3 and 3.8: the runs in theleft half typically produce a higher number of rejected (overgeneralized) samples.Conversely, one expects a greater tendency toward overfitting in the training runs using more thanthe minimal number of states. The plots in the middle rows of Figures 3.3 and 3.8 confirm this expectation:the right halfs show a greater number of rejected strings from the target language, indicating insufficientgeneralization.It is conceivable that for each language there exists a model size that would lead to a goodcompromise between generalization and data fit so as to produce reliable structure estimation. The problemis that there seems to be no good way to predict that optimal size. 15Successful use of the model merging approach also relies on suitable parameter choices, mainly ofthe global prior weight (or the number of ‘effective samples’). The prime advantage of merging in this regardis that the parameters seem to be more robust to both sample size and distribution, and the mechanics of thealgorithm make it straightforward to experiment with them. Furthermore, it appears that overgeneralizationby excessive merging tends to produce ‘plausible’ models (with the obvious caveat that this conclusion isboth tentative given the limited scope of the investigation, and a matter of human judgment).3.6.2 Phonetic word models from labeled speech3.6.2.1 The TIMIT databaseIn the second evaluation stage, we were looking for a sizeable collection of real-world data suitablefor HMM modeling. The TIMIT (Texas Instruments-MIT) database is a collection of hand-labeled speechsamples compiled for the purpose of training speaker-independent phonetic recognition systems (Garofolo1988). It contains acoustic data segmented by words and aligned with discrete labels from an alphabet of62 phones. For our purposes, we ignored the continuous, acoustic data and viewed the database simply as acollection of string samples over a discrete alphabet.The goal is to construct a probabilistic model for each word in the database, representing itsphonetic structure as accurately as possible, i.e., maximizing the probabilities of the observed pronunciations.A fraction of the total available data, the test set, is set aside for evaluating the induced models, while the rest isused to induce or otherwise estimate the probabilistic model for each word. By comparing the performance ofthe models generated by various methods, along with other relevant properties such as model size, processingtime, etc., we can arrive at a reasonably objective comparison of the various methods. Of course, the ultimate15 In Section 3.6.2 we actually use a simple heuristic that scales the model sizes linearly with the length of the samples. This heuristicworks rather well in that particular application, but it crucially relies on the models being loop-free, and hence wouldn’t apply generally.
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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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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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