Large Vocabulary Continuous Speech Recognition - Berlin Chen

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Linear Lexicon vs. Tree Lexicon(for Word-Conditioned Search)• In the general n-gram case (n ≧1), the total number ofprefix tree copies is equal to |V| n−1 , where |V| is the lexiconsize and n the LM order• When using a (flat) linear lexicon and in the general n -gram case (n ≧2), the total number of copies of the linearlexicon would be |V| n−2|V| linear lexicons for trigram modeling|V| 2 tree-copies for trigram modelingSpeech – Berlin Chen 12
Context-Dependent Phonetic Constraints• Context-Dependent modeling (e.g., cross-word, CW,triphone modeling) is a crucial factor regarding thesearch space at the junction between successive wordsImplementation of CWwould be much more difficultfor time-conditioned thanword-conditioned search(why ?)• Current NTNU system is implemented with intra-wordINITIAL/FINAL or triphone modelingSpeech – Berlin Chen 13
Page 1 and 2: Large Vocabulary ContinuousSpeech R
Page 3 and 4: Why LVCSR Difficult ? (2/2)• Maxi
Page 5 and 6: Classification of Approaches to LVC
Page 7 and 8: Word-conditioned vs. Time-condition
Page 9 and 10: Word-Conditioned Tree-Copy Search (
Page 11: Lexical/Phonetic Tree (2/2)• Reas
Page 15 and 16: Word-Conditioned Search (1/3)• Wo
Page 17 and 18: Word-Conditioned Search (3/3)Backtr
Page 19 and 20: WC: Implementation Issues (2/3)•
Page 21 and 22: WC: Pruning of Path Hypotheses (1/5
Page 27 and 28: WC: Language Model Look-ahead (2/2)
Page 29 and 30: WC: Acoustic Look-ahead (2/3)• Po
Page 31 and 32: More Details on Time-Conditioned Se
Page 33 and 34: TC: Decomposition of Search History
Page 35 and 36: TC vs. WC: Conflicting Expectations
Page 37 and 38: TC vs. A* search (2/2)- Operations
Page 39 and 40: Word Graph Rescoring (1/6)• Tree-
Page 41 and 42: Word Graph Rescoring (3/6)• If bi
Page 43 and 44: Word Graph Rescoring (5/6)• “Wo
Page 45: • Feature ExtractionConfiguration

Large Vocabulary Continuous Speech Recognition - Berlin Chen

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