Large Vocabulary Continuous Speech Recognition - Berlin Chen

More documents

Recommendations

Info

TC: Decomposition of Search History (1/3)If only the latest m-1 word history is instead kept:hGt t( w;τ , t) = max p( x , s w)tτ + 1s= conditiona l prob. that word w produces xnnt t n( w ; t) = P( w ) max p( x , s w )11= joint prob. of observingt1sτ + 1τ + 1If the whole word history is kept:G11x1t1andnn−1n−1( w1; t) = max{ P( wnw1) G( w1; τ ) h( w;τ , t)}τn−1n−1= P( w w ) ⋅ max{ G( w ; τ ) h( w;τ , t)}n1τ1wn1tτ + 1ending at tGHnm( w1; t) ≈ H ( v2; t)m( v ; t)= max⎧Pn n m⎨1w1: w n − m + 2 = v2⎩(with DP recursion )= maxv21nt t n( w ) ⋅ max p( x , s w )t1sm−1m−1{ P( vmv1) ⋅ max{ H ( v1; τ ) h( w;τ , t)}τ111⎫⎬⎭Speech – Berlin Chen 32
TC: Decomposition of Search History (2/3)• Tree-internal search hypotheses (for an internal node,not word-end node, s)If the whole word history is kept:Qτn ⎧t t n( t;s) = max P( w ) ⋅ max p( x , s w )==wwwmax Pn1max Gn1n1n ⎧τ τ n ⎫t t( w ) ⋅ max p( x , s w ) ⋅ max p( x , s −)1⎨⎩t1⎨⎩s : s = S⎬⎭nt t( w ; τ ) ⋅ max p( x , s −)11tτ + 1sτt1s : s = S: s = stτwnw n, s = st1τ + 1111τ + 11tτ + 1s1⎫⎬⎭: s = sIf only the latest m-1 word history is instead kept:Qτnt t( t;s) = max G( w ) ⋅ p( x+s −)n 1; τ maxtτ 1,τ + 1w1sτ+ 1:st= smt t≈ max H ( v ) ⋅ p( x+s+−)m m n 2; τ maxtτ 1,τ 1v2: v2= w n − m + 2sτ+ 1:st= st t= H () τ ⋅ max p( x , s −)maxtτ + 1s: s = stτ + 1τ + 1tτ + 1τ + 1Word identity is still unknownSpeech – Berlin Chen 33
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: More Details on Time-Conditioned Se
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

Create successful ePaper yourself

Delete template?

Save as template?