Sinovoice-2016-3-17

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

Big-Model Training

• 16k-10000h DNN training 7*1024-10000

• MPE training to be done
• Performance about 1 point worse than 7*2048-1000
• The decoding speed is similar with old-nnet1 version
• 7*2048-1000 net weight-matrix factoring, to improve the decoding speed --SVD

• TDNN+LSTM chain training

• Beam-13 performance, chain is better, but beam-9 worse.

• 8K

• HuaWei

• 5000h 7*1024-XENT be training, performance is similar to 7*2048
• To train MPE base on current XENT-model

Embedding

• 10000h-chain 5*400+800 DONE.

• To confirm the beam affection on the performance

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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• Recording

Character LM

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

• Union
• Compose

Path Emphasize

• Script Done.

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)