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

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CHAPTER 3. HIDDEN MARKOV MODELS 66t1.000.09tcl0.64nx0.180.12ax0.09ix1.000.300.871.000.190.27ih1.00n1.00End0.511.000.030.161.001.00Start0.03awao1.00f0.03ahen0.38 0.080.711.000.031.00q0.29aaepiFigure 3.13: Merged HMM constructed from 37 samples of the word often.Merging was constrained to keep the emission on each state unique.
CHAPTER 3. HIDDEN MARKOV MODELS 67no. states25201510505 10 15 20 25 30 35no. samplesFigure 3.14: Number of HMM states as a function of the number of samples incorporated during incrementalmerging.Each spike represents the states added to model an unparseable sample, which are then (partly)merged into the existing HMM structure.1êfor the HMM. In a single-output HMM, each emission carries a prior • probability of • , rather than one of thevarious structural priors over multinomials discussed in Section 3.3.3.3. Incidentally, the constraint can alsospeed up the algorithm significantly, because many candidates are efficiently eliminated that would otherwisehave to be evaluated and rejected. This advantage by far outweighs the larger size of the derived models.A second constraint can be enforced in this particular domain (and possibly others). Since theresulting HMMs are meant to be phonetic word models, it does not make sense to allow loops in these models.In very rare circumstances the merging algorithm might be tempted to introduce such loops, e.g., becauseof some peculiar repetitive pattern in a sample (‘banana’). Given our prior knowledge in this regard we cansimply rule out candidate merges that would introduce loops. 17HMM merging can thus be used to derive models for allophonic variation from data, withoutexplicitly representing a mapping from individual phonemes to their realizations.This is in contrast toother approaches where one first induces rules for pronunciations of individual phonemes based on theircontexts (e.g., using decision tree induction), which can then be concatenated into networks representing wordpronunciations (Chen 1990; Riley 1991). A detailed comparison of the two approaches is desirable, but so farhasn’t been carried out. We simply remark that both approaches could be combined by generating allophone17 It is customary in HMM modeling for speech recognition to introduce self-loops on states to model varying durations. This does notcontradict what was said above; these self-loops are introduced at a lower representational level, along with other devices such as statereplication. They are systematically added to the merged HMM before using the HMM for alignment with continuous speech data.
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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 1. INTRODUCTION 4A.0.830.33
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CHAPTER 1. INTRODUCTION 6the ¨ 0 l
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Page 116 and 117: 104Chapter 5Probabilistic Attribute
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CHAPTER 5. PROBABILISTIC ATTRIBUTE
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122Chapter 6Efficient parsing with
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1z1CHAPTER 6. EFFICIENT PARSING WIT
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and each state in set ( -ÏH ):6)
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NPDetVTVI P CHAPTER 6. EFFICIENT PA
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) The probabilistic unit-production
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The forward and inner probabilities
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,,by nonterminals. Multiplying this
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6CHAPTER 6. EFFICIENT PARSING WITH
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description. Again, we ignore this
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for all pairs of states d =¸+= Š
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,9 ¸0 : 0¸ £j9¸ A )z9£CHAPTER
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are then summed over all nontermina
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1CHAPTER 6. EFFICIENT PARSING WITH
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168Chapter 7-grams from Stochastic
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CHAPTER 7. -GRAMS FROM STOCHASTIC
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-grams CCHAPTER 7. -GRAMS FROM ST
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,,CHAPTER 7. -GRAMS FROM STOCHASTI
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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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