Sinovoice-2016-4-7

Data

• 16K LingYun

• 2000h data ready
• 4300h real-env data to label

• YueYu

• Total 250h(190h-YueYu + 60h-English)
• CER: 75%

• WeiYu

• 50h for training
• 120h labeled ready

Model training

Big-Model Training

• 7*2048-10000 net weight-matrix factoring, to improve the decoding speed --SVD

• SVD looks OK, but fine-tuning still didn't work.

 Base WER:
 relu_2000_mpe_1000H: 17.72
 relu_1200_mpe_1000H: 18.60

 |layer / nodes retaind|  200  |  400  |  600  |  800  | 1000  | 1200  | 1400  |  1600  |
 |      hidden 2       |       |       | 22.53 | 20.30 | 19.01 |       |       |        |
 |      hidden 7       |       | 18.92 | 18.30 | 17.92 |       |       |       |        |     
 |       final         |       |       | 18.32 | 18.00 | 17.83 |       |       |        |     

Embedding

• 10000h-chain 5*400+800 DONE.

• Beam affect the performance of chain model significantly, need more investigation.

SinSong Robot

• Test based on 10000h(7*2048-xent) model

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   condition | clean  | replay(0.5m) | real-env
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     wer     |   3    |  18(mpe-14)  | too-bad
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• Plan to record in restaurant on April 10.

Character LM

• Except Sogou-2T, 9-gram has been done.
• Worse than word-lm(9%->6%)
• Add word boundary tag to Character-LM trainig
• Merge Character-LM & word-LM

• Union
• Compose, success.

• 2-step decoding: first, character-based LM. Then, word-based LM.

SID

Digit

• Same Channel test EER: 100%

• Speaker confirm
• phone channel

• Cross Channel

• Mic-wav PLDA adaptation EER from 9% to 7% (20-30 persons)