Speech recognition using neural networks - Chapter 8 potx
Speech recognition using neural networks - Chapter 8 potx
... 111 HMM-1 55% HMM-5 96% 71% 58% 76% HMM-10 97% 75% 66% 82 % LPNN 97% 60% 41% HCNN 75% LVQ 98% 84 % 74% 61% 83 % TDNN 98% 78% 72% 64% MS-TDNN 98% 82 % 81 % 70% 85 % Table 8. 1: Comparative results on ... MS-TDNN that we achieved a word recognition accuracy of 90.5% using only 67K parameters, significantly outperforming the context inde- pendent HMM systems while requiring fewer param...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 3 potx
... that the neural network may be simulated on a conventional computer, rather than imple- mented directly in hardware. 3. Review of Neural Networks 50 1 982 ) — or alternatively by neural networks ... Delay Neural Network (TDNN), shown in Figure 3 .8. This architecture was initially developed for phoneme recognition (Lang 1 989 , Waibel et al 1 989 ), but it has also been applied...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 1 pot
... 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 89 0 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 conventional...
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 1 087 , 486 ,2502, 986 , 381 4,2715 generalized triphone (4000) MARKET M 1 ,M 2 ,M 3 ; A 1 ,A 2 ,A 3 ; M = 384 3,2257,1056; A = 189 4,1247, 385 2; senone (4000) 2. Review of Speech Recognition 22 2.3.3. ... /ts/ 2. Review of Speech Recognition 26 2.3.4. Limitations of H...
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-processor ar...
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
... (100%) 924 1 106/1 18 (90%) 55/60 (92%) 85 5/900 (95%) 2 116/1 18 ( 98% ) 58/ 60 (97%) 88 6/900 ( 98% ) 3 117/1 18 (99%) 60/60 (100%) 89 1/900 (99%) Table 6.1: LPNN performance on isolated word recognition. 6.2. ... HMMs using a single gaussian mixture, vs. LPNN. perplexity System 7 111 402 HMM-1 55% HMM-5 96% 70% 58% HMM-10 97% 75% 66% LVQ 98% 80 % 74% LPNN 97% 60% 40% Table 6.4:...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 7 pdf
... labeling. Figure 7.14: A 3-state phoneme model outperforms a 1-state phoneme model. 80 82 84 86 88 90 92 94 96 98 100 word accuracy (%) 0 1 2 3 4 5 epochs 1-state vs 3-state models 1 state per ... Recursive labeling optimizes the targets, and so improves accuracy. 82 84 86 88 90 92 94 96 98 100 word accuracy (%) 0 1 2 3 4 5 6 7 8 9 10 epochs Recursively trained labels (9/13/9...
Ngày tải lên: 13/08/2014, 02:21
Speech recognition using neural networks - Chapter 9 pptx
... boundaries 87 LPC coefficients 10, 115 LPNN 75, 81 89 , 94, 14 7-1 48 basic operation 81 82 training & testing procedures 8 2 -8 4 experiments 8 4 -8 7 extensions 8 9-9 4 weaknesses 9 4-9 9 vs. HMM 88 89 , 14 7-1 48 LVQ ... 1 28 tied 38, 110 training 3 5-4 8 update frequency 48, 82 , 106, 12 8- 9 , 144 Widrow-Hoff rule 36 window of speech 51, 6 3-6 4, 77...
Ngày tải lên: 13/08/2014, 02:21
speech recognition using neural networks
... performance. We will see that neural networks help to avoid this problem. 1.2. Neural Networks Connectionism, or the study of artificial neural networks, was initially inspired by neuro- biology, but it ... Delay Neural Network (TDNN), shown in Figure 3 .8. This architecture was initially developed for phoneme recognition (Lang 1 989 , Waibel et al 1 989 ), but it has also been...
Ngày tải lên: 28/04/2014, 10:18