Large Vocabulary Continuous Speech Recognition - Berlin Chen

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Word-conditioned vs. Time-conditioned Search (3/3)Word-conditioned Search (with bigram LM)Time-conditioned Search (with m-gram LM)These two algorithms are stated in a “look-backward” manner.Speech – Berlin Chen 8
Word-Conditioned Tree-Copy Search (WC)• As trigram language modeling is usedP(‧| sil sil)P(‧| sil 飛蛾 )飛蛾P(‧| 飛蛾一群 ) P(‧| 一群影響 ) P(‧| 影響生活 )P(‧| 生活很大 )一群影響生活很大造成P(‧| 生命很大 )很大Node( history, arc, state)P(‧| 很大造成 )肺炎疫情影響生命頗大P(‧| sil 肺炎 )P(‧| 肺炎疫情 ) P(‧| 疫情影響 ) P(‧| 影響生命 ) P(‧| 生命頗大 )V trees V 2 trees V 2 trees V 2 trees V 2 treesLanguage Model Look-aheadAcoustic Look-aheadcan be applied– The pronunciation lexicon is structured as a tree– Due to the constraints of n-gram language modeling, a word’soccurrence is dependent on the previous n-1 words– Search through all possible tree copies from the start time to theend time of the utterance to find a best sequence of wordhypothesesSpeech – Berlin Chen 9
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: Word-conditioned vs. Time-condition
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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