Sinovoice-2014-12-10

DNN training

Environment setting

• Another 3 3T disks are ready for RAID-0.
• Another GPU machine was purchased. Add 4 3T disks to construct RAID-0.

Corpora

• Scripts for confidence generation is ready for auto transcription.
• 300h telephone speech data (Sinovoice recording) were done.
• Adaptation data 900 sentences ready.

470 hour 8k training

• 300h incremental training (IT) 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
8k states + IT	-	20.04/17.38	20.01/17.32	20.07/17.44	19.94/17.65

6000 hour 16k training

• Ran CE DNN to iteration 5 (8400 states, 80000 pdf)
• Testing results go down to 13.77% WER (Sinovoice results: 11.78).

Model	WER	RT
small LM, it 4, -5/-9	15.80	1.18
large LM, it 4, -5/-9	15.30	1.50
large LM, it 4, -6/-9	15.36	1.30
large LM, it 4, -7/-9	15.25	1.30
large LM, it 5, -5/-9	14.17	1.10
large LM, it 5, -5/-10	13.77	1.29

Adaptation

• Code ready for direct adaptation, insertion adaptation and KL-regularized adaptatoin
• 50 sentences for adaptation, 834 sentences for testing
• WER from 14.56 to 11.13
• Hidden layer adaptation is better than input and output adaptation
• Before Linear adaptation is better than after-linear adaptation
• Results are here

DNN Decoder

• Comparison between CLG and HCLG decoder

• CLG decoder uses less memory in decoding
• HCLG is faster and more accurate than CLG, and more amiable to beam control here

• Faster decoder
• std::exp/std::log result in very slow computation in train203. Solved the problem by replacing to standard exp() and log().
• The RT of the latest decoder on train203 is 0.25