Слайд 1

More documents

Recommendations

Info

6.7 Crawling Experiment Language Model Website Size LS SMT OOV Rate 34 Statistical Machine Translation Language specific rules Out of vocabulary PPL Base Baseline 1.9M 10.35% 4654.21 LM1 rian.ru 4.2M x x 25.59% 3447.27 LM2 inosmi.ru 792K x x 13.39% 5341.18 LM3 Inosmi.ru 920K x x 46.84% 2487.77 rian.ru 53M 14.23% 4866.73 rian.ru 57M x 13.38% 4644.32 LM5 rian.ru 58M x x 13.51% 4809.67 LM4 vesti.ru 13M x x 23.53% 4639.72 Last interpolated 7.9% 3325 Perplexity
7. Results Baseline Eval(Test): 58.17% ---------------------------------------- Baseline: 60.00% Base + LM1: 52.05% Base + LM1 + LM2: 49.79% on Dev Set Base + LM1 + LM2 + LM3: 49.79% Base + LM1 + LM2 + LM4: 49.82% Base*0.37 + LM1*0.32 + LM2*0.04 + LM5*0.27: 44.78% ----------------------------------------- LM1: inosmi.ru 792K LM2: rian.ru: 4.2M LM3: inosmi.ru: 920K LM4: vesti.ru 13M LM5: rian.ru 58M 35
Page 1 and 2: Development of an automatic speech
Page 3 and 4: 1. Goal • Build speech recognizer
Page 5 and 6: 2.2 Automatic speech recognition (A
Page 7 and 8: 2.4 Automatic speech recognition (A
Page 9 and 10: 3. Rapid Language Adaptation Toolki
Page 11 and 12: 4. Russian language • Russian is
Page 13 and 14: 5. Previous results Ngoc Thang Vu,
Page 15 and 16: 6. ASR building steps 1. Pronunciat
Page 17 and 18: 6.2 Phonemes • The International
Page 19 and 20: 6.2 Phonemes Dictionary with MM7 ph
Page 21 and 22: 6.3 Speech data The Russian speech
Page 23 and 24: 6.4 Acoustic model All speech data
Page 25 and 26: 6.5 Language model What is expected
Page 27 and 28: 6.5 Language model: interpolation S
Page 29 and 30: 6.6 Text normalization Text normal
Page 31 and 32: 6.6 Text normalization: SMT normali
Page 33: 6.7 Crawling need MORE data —
Page 37 and 38: 6.7 Crawling: Experiment The same w
Page 39 and 40: Suggestions ● Better Russian text
Page 41: References [Vu et al, 2010] Ngoc T

Слайд 1

Create successful ePaper yourself

Delete template?

Save as template?