Sinovoice-2014-04-15

Environment setting

• Sinovoice internal server deployment. Now a better approach by using Gitlab + Redmine.
• Delivered part of the Kaldi code. Some fix yet waiting for check in.
• Email notification is problematic. Need obtain a smtp server

Corpora

• 300h Guangxi telecom text transcription prepared. 150h before 18th, April.
• Now totally 1338h (470 + 346 + 105BJ mobile + 200 PICC + 108h HBTc + 109h New BJ mobile) telephone speech is ready.
• 16k 6000h data: 978h online data from DataTang + 656h online mobile data + 4300h recording data.
• Standard established for LM-speech-text labeling (speech data transcription for LM enhancement)
• Xiaona will prepare noise database. Start from telephone speech.

Acoustic modeling

Telephone model training

1000h Training

• Baseline: 8k states, 470+300 MPE4, 20.29
• Jietong phone, 200 hour seed, 10k states training:

• Xent 16 iteration: 22.90
• MPE1 : 20.89

• CSLT phone, 8k states training

• MPE1: 20.60
• MPE2: 20.37
• MPE3: 20.37
• MPE4: 20.37

6000 hour 16k training

Training progress

• 6000h/CSLT phone set training

• Xent: 12.83
• MPE1: 9.21
• MPE2: 9.13

• 6000h/jt phone set phone set training

• ran into MPE1.

Train Analysis

• The Qihang model used a subset of the 6k data

• 2500+950H+tang500h*+20131220, approximately 1700+2400 hours

• GMM training using this subset achieved 22.47%. Xiaoming's result is 16.1%.

• Seems the database is still not very consistent
• Xiaoming kicked off the job to reproduce the Qihang training using this subset

Multilanguage Training

• Prepare Chinglish data: contacted with a vendor for 1000 hour mobile recording. Will check how much we need
• AMIDA database downloading
• Build a baseline system
• Prepare shared DNN structure for multilingual training

Noise robust feature

• GFbank can be propagated to Sinovoice

• Let Mengyuan prepare the experiments

• Liuchao will prepare fast computing code

Language modeling

Training recipe transfer

• Training process was delivered.
• Problems in encoding were solved.
• Initial CSLT LM buildup completed.

Domain specific atom-LM construction

Some potential problems

• Unclear domain definition
• Using the same development set (8k transcription) is not very appropriate

Text data filtering

• Prepare word list
• VSM-based topic segmentation was delivered to Sinovoice, but the tool is highly inefficient.
• An enhanced toolkit was delivered.
• A telecom specific word list is ready, several stop words are ready

DNN Decoder

decoder optimization

• Test computation cost of each step

• beam 9/5000: netforward 65%
• beam 13/7000: netforward 28%

• Sinovoice change in Kaldi delivered and ready to check-in
• Need to verify the speed of the CSLT engine

Frame-skipping

• Zhiyong & Liuchao will deliver the frame-skipping approach.

BigLM optimization

• Investigate BigLM retrieval optimization.