2013-05-10

Data sharing

• LM count files still undelivered!

DNN progress

Experiments

• setups for input layer

s1: mfcc(13), splice +-5[143]

s2: mfcc(13), splice +-5(143), LDA[143]

s3: mfcc(13), delta(39), splice +-5(429), LDA[143]

s4: mfcc(13), delta(39), splice +-5(429), LDA[300]

• setups for alignment

tri1: triphone training, feature input: mfcc(13), delta[39]. #pdfs 1651, #gaussians 10028

tri2: LDA/MLLT training, feature input: mfcc(13), delta(39), splice +-4(351), LDA[40]. #pdfs 3536, #gaussians 39995

• other notes

about 100 hours training data

88k LM, biglm decoding (1e-5 / 1e-9)

gpu-based nnet training, in-1200-1200-1200-1200-out

• results

Test Set	fMMI	s1/tri1	s2/tri1	s3/tri1	s4/tri1	s2/tri2	s4/tri2	cpu-based (like s4/tri1)
map		25.38	24.47	26.16	26.20	22.85	24.27	26.45
2044		23.58	22.82	23.84	24.13	21.45	22.76	24.66
notetp3		16.08	14.89	15.92	15.97	14.89	14.79	16.14
1900		8.55	8.43	8.66	8.90	7.30	7.91	8.23
general		36.18	34.79	35.88	35.90	33.06	33.79	38.02
online1		34.68	33.90	33.45	33.38	32.93	32.43	33.00
online2		27.27	26.61	26.26	26.36	25.94	25.69	26.63
speedup		24.97	24.40	24.55	25.42	23.04	23.67	27.17

• conclusion

1. the GPU approach is comparable with the CPU approach (see s4/tri1 & CPU results). The former works slightly better in most cases.
2. the fine training leads to significant better performance than the rough training (see s2/tri1 vs s2/tri2 & s4/tri1 vs s4/tri2)
3. the delta features do not help, actually harm the perfromance (see s2/tri1 vs s3/tri1 & s4/tri1)
4. the linear LDA helps the performance (see s1/tri1 vs s2/tri1)
5. the best system is s2/tri2: with out delta, apply a linear LDA.

Tencent exps

TODO

GPU & CPU merge

1. current schedule is to migrate GPU to CPU. Will start to code in this week.

L-1 sparse initial training

• experiments on L1 and L2 penalty

1. LM: 4G gigabyte LM, 1e-5 small LM
2. AM: 100hour tri4b_nn

Test Set	0	1.00E-06	1.00E-05	2.50E-05	5.00E-05	7.50E-05	1.00E-04
map	61.06	61.18	61.31	61.72	62.89	62.84	62.48
2044	49.53	49.58	49.84	49.94	50.71	51.08	51.08
notetp3	43.44	43.44	43.55	44.58	45.22	44.95	45.76
1900	38.50	38.54	39.00	39.21	40.33	40.53	40.60
general	61.24	61.22	61.69	61.84	62.71	62.83	62.93
online1	58.02	58.05	58.31	58.23	58.83	59.06	59.46
online2	53.62	53.70	54.03	53.93	54.65	54.94	55.51
speedup	57.51	57.49	57.93	58.31	59.75	60.08	59.52

• Conclusions

1. L1 and L2 penalty do not work in the current nnet-GPU code.
2. will work to check the L1 code and change the penalty scheme.

Kaldi/HTK merge

• HTK2Kaldi: hold.
• Kaldi2HTK: stuck. various sp models tried but don't help
• Need thorough debug this week.

Embedded progress

• Status:

1. first embedded demo done, 1000 words take 3.2M memory.
2. accuracy test not yet finished
3. training acoustic model for sphinx

• To be done

1. finish AM training
2. run offline test