Sinovoice-2014-01-20

DNN training

Environment setting

• Accounts re-arrangement done on the SGE cluster. NO ROOT TO WORK.
• Changed NFS server to 40 processes, hope to increase disk reading.
• Agree to withdraw root/sudo privilege.
• Agree to create a RAID-0 with another 3 3T disks

Corpora

• Changed the data labeling strategy: gender and noise length will not be labelled for the following several corpora.
• Automatic labeling

• Xiaoming will work with Zhiyong to discover how to generate transcriptions with confidence score held.
• The first step is to investigate the raw accuracy on the domain-dependent test, and then decide if it is appropriate to use automatic labeling

• Xiao Na will prepare 300h telephone speech data (Sinovoice recording). This will be used to improve the 8k model.

470 hour 8k training

• MPE training done

Model	CE	MPE1	MPE2	MPE3	MPE4
4k states	23.27/22.85	21.35/18.87	21.18/18.76	21.07/18.54	20.93/18.32
8k states	22.16/22.22	20.55/18.03	20.36/17.94	20.32/17.78	20.29/17.80

6000 hour 16k training

• Feature extraction done: solved several problems in the data: (1) short wave (2) unmatched file length (3) unmatched sample rate.
• Training has gone to tri4b, quick increase of states/pdfs.
• DNN training will be started on Tuesday.

DNN Decoder

• Sinovoice decoder: some errors in FST building. Many triphones were lost after C composing. Problems in cdgen?
• Kaldi decoder:

• A minor difference between CLG/HCLG results was found. Debugging into the problem.
• CLG RT is comparable to the HCLG, roughly 0.3-0.4 in CSLT grid-2.
• Additional optimization on pdf-pre-computing will be investigated.
• Code deliver today.