Large Vocabulary Continuous Speech Recognition - Berlin Chen

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WC: Acoustic Look-ahead (3/3)• Tested on 4-hour radio broadcast news (Chen et al., ICASSP 2004)– Use Kneser-Ney backoff smoothing for language modeling• The recognition efficiency for TC improves significantly (a relativeimprovement of 41.61% was obtained) while the time spent onacoustic look-ahead (0.004 real time factor) was almost negligibleSpeech – Berlin Chen 30
More Details on Time-Conditioned Search
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 and 12: Lexical/Phonetic Tree (2/2)• Reas
Page 13 and 14: Context-Dependent Phonetic Constrai
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: WC: Acoustic Look-ahead (2/3)• Po
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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