Large Vocabulary Continuous Speech Recognition - Berlin Chen

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Word Graph Rescoring (4/6)• Bookkeeping at the word level– When word hypotheses were recombined into one hypothesis tostart up the next tree• Not only the word segments (arcs) with the best predecessorword were recorded• But for the hypotheses that have the same LM history, onlythe best one was kept (“word-pair” approximation)SIL (-)肺炎 (-)肺炎 (SIL)疫情 ( 飛蛾 )疫情 ( 肺炎 )一群 ( 飛蛾 )疫情 ( 肺炎 )隱藏 ( 疫情 )隱藏 ( 一群 )影響 ( 疫情 )影響 ( 疫情 )生活 ( 隱藏 )SIL( 生活 )生活 ( 影響 )SIL( 生命 )生命 ( 影響 )生命 ( 影響 )影響 ( 一群 )飛蛾 (SIL)一群 ( 肺炎 )Speech – Berlin Chen 42
Word Graph Rescoring (5/6)• “Word-Pair” Approximation: for each path hypothesis, theposition of word boundary between the last two words isindependent of the other words of this path hypothesis• An illustration for “word-pair” approximation肺炎疫情影響飛蛾疫情影響B uv( w , t ) = τ2u nw=“ 影響 ”τ τ12u n-1v=“ 疫情 ”u n-2Historyb=“ 飛蛾 ” a=“ 肺炎 ”Speech – Berlin Chen 43
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 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: Word Graph Rescoring (3/6)• If bi
Page 45: • Feature ExtractionConfiguration

Large Vocabulary Continuous Speech Recognition - Berlin Chen

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