Large Vocabulary Continuous Speech Recognition - Berlin Chen

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Why LVCSR Difficult ? (1/2)• The software complexity of a search algorithm isconsiderably high• The effort required to build an efficient decoder is quitelargeSpeech – Berlin Chen 2
Why LVCSR Difficult ? (2/2)• Maximum Approximation of the Decoding ProblemWˆ==arg maxWp(X| W)P(W)⎪⎧P Wnp sTWn ⎪⎫⎨ (1) ∑ ( X,1|1) ⎬W:W nsT1 1⎪ ⎭arg max⎪⎩Wˆ( sT: the correspond ing state sequence of Wn )1⎧≈ arg max ⎨P(WW W n⎩n1)αmax: sT11p(X,sT1| Wn1⎫) ⎬⎭1( α : heuristic language model factor )Speech – Berlin Chen 3
Page 1: Large Vocabulary ContinuousSpeech R
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 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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