Speech recognition using neural networks - Chapter 3 potx
Speech recognition using neural networks - Chapter 3 potx
... ) 2 i ∑ =x j y i w ji i ∑ = y 1 y 2 y j x j w y y y 1 y 2 y 1 y 2 w 3 w 4 w 5 w 3 w 4 w 5 y 1 y 2 y 4 x 4 y 3 x 3 y 5 x 5 y k x k w 3 w 4 w 5 w k (a) (b) x j y i w ji –( ) 2 i ∑ =x j y i w ji i ∑ = x j y j x k y j w kj j ∑ = 3. Review of Neural Networks 46 By ... away from x: (34 ) 3. 3.1.2. Recurrent Networks Hopfield (1982) studied neural networks that implement a kin...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 8 potx
... for a tied-mixture HMM. • TDNN: Time Delay Neural Network (Section 3. 3.1.1), but without temporal inte- gration in the output layer. This may also be called an MLP (Section 7 .3) with hier- archical ... follows: • HMM-n: Continuous density Hidden Markov Model with 1, 5, or 10 mixture den- sities per state (as described in Section 6 .3. 5). • LPNN: Linked Predictive Neural Network (Secti...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 1 pot
... volumes, and so on, x Speech Recognition using Neural Networks Joe Tebelskis May 1995 CMU-CS-9 5-1 42 School of Computer Science Carnegie Mellon University Pittsburgh, Pennsylvania 1521 3- 3 890 Submitted ... that neural networks can indeed form the basis for a general pur- pose speech recognition system, and that neural networks offer some clear advantages over conve...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 2 docx
... (200) M,A,R,K,E,T $ M, M A, A R, R K, K E, E T $ M A , M A R , A R K , R K E , K E T , E T $ MAR,KET MA,AR,KE,ET 1087,486,2502,986 ,38 14,2715 generalized triphone (4000) MARKET M 1 ,M 2 ,M 3 ; A 1 ,A 2 ,A 3 ; M = 38 43, 2257,1056; A = 1894,1247 ,38 52; senone (4000) 2. Review of Speech Recognition 22 2 .3. 3. Variations There ... ) i ∑ = α j (t) t-1 t α i (t-1) . . . . a ij b j...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 4 pps
... by a simple HMM-based recog- nizer. Figure 4 .3: Time Delay Neural Network. Integration Speech input Phoneme output B D G B D G 4 .3. NN-HMM Hybrids 63 and neural networks; the speech frames then ... 26.0% error for speaker-dependent recognition, and 30 .8% versus 40.8% error for multi-speaker recognition. Training time was reduced to a reasonable level by using a 64-processo...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 5 doc
... no English. Janus performs speech trans- lation by integrating three modules — speech recognition, text translation, and speech gen- eration — into a single end-to-end system. Each of these modules ... The speech recognition module, for exam- ple, was originally implemented by our LPNN, described in Chapter 6 (Waibel et al 1991, Osterholtz et al 1992); but it was later replaced b...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 6 pps
... continuous speech. Net 1 Net 2 Net 3 1 2 3 4 5 6 6 .3. Linked Predictive Neural Networks 87 Since our database is not phonetically balanced, we normalized the learning rate for differ- ent networks ... LPNN, however, which used a 6-state phoneme model imple- mented by 3 networks, this context-dependent HCNN used the 5-state phoneme model shown in Figure 6.9 (or a 3- state mode...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 7 pdf
... (%) 0 1 2 3 4 5 epochs 36 00 train, 39 0 test. (Aug24) FFT-16 FFT -3 2 (with deltas) PLP-26 (with deltas) LDA-16 (derived from FFT -3 2 ) 7 .3. Frame Level Training 121 7 .3. 4.1. Learning Rate Schedules The ... training of a speech recognizer?” 7.5. Summary 1 43 7.5. Summary In this chapter we have seen that good word recognition accuracy can be achieved using neural networks...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 9 pptx
... 26 vs. NN-HMM 8 8-8 9, 14 7-1 50, 15 3- 1 54 homophones 73, 84 Hopfield network 39 hybrid network architectures 36 , 43 hybrid systems. See NN-HMM hyperplanes 3 3- 3 5, 49 hyperspheres 3 3- 3 5, 39 , 42, 43 Bibliography 162 [40] ... 128 TDNN 38 , 53 design considerations 38 , 60, 11 0-2 , 152 experiments 5 3- 5 6, 14 7-1 48 and MS-TDNN 61, 102, 13 8-9 , 14 7-8 t...
Ngày tải lên: 13/08/2014, 02:21
speech recognition using neural networks
... class y j is active. 3. 3. A Taxonomy of Neural Networks Now that we have presented the basic elements of neural networks, we will give an over- view of some different types of networks. This overview ... ) i ∑ = α j (t) t-1 t α i (t-1) . . . . a ij b j (y t ) i j y 1 T y 1 3 A: 0.2 B: 0.8 A: 0.7 B: 0 .3 0.4 0.6 1.0 1.0 .1764 j=0 j=1 t=0 .42 . 032 0.0 .08 .0496 .096 t=1 t=2 t =3...
Ngày tải lên: 28/04/2014, 10:18