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UKspeech2017
mi.eng.cam.ac.uk/UKSpeech2017/posters/y_wang.pdf17 Nov 2017: L1, proficiency level, recordingSpontaneous responses increase difficulty, e.g. disfluenciesTranscribing is challenging inter-annotator error rate about 24.7%. -
Deep Learning for Speech Recognition
mi.eng.cam.ac.uk/~mjfg/LxMLS17.pdf29 Nov 2017: Network Interpretation [24]. Standard /ay/ Stimulated /ay/. • Deep learning usually highly distributed - hard to interpret• awkward to adapt/understand/regularise• modify training - add stimulation regularisation• improves ASR performance. -
Template.dvi
mi.eng.cam.ac.uk/~ar527/chen_icassp2017a.pdf22 Mar 2017: Mongolian FLP 511K 24.0K - 4.19 12.19WEB 139M 199.8K 0.93 2.10 5.62. ... 24,no. 11, pp. 2146–2157, 2016. [15] Xie Chen, Yongqiang Wang, Xunying Liu, Mark Gales, andP. -
.poster_jeremy_v2.tex.dvi
mi.eng.cam.ac.uk/UKSpeech2017/posters/j_wong.pdf17 Nov 2017: 207Vseparate 45.8 46.0 46.6MT 47.7 47.8 47.3MT-TS 45.7 45.7 46.3. AMIseparate 24.5 24.6 24.6MT 25.4 25.5 ... 25.1MT-TS 24.3 24.4 24.6. -
STIMULATED TRAINING FOR AUTOMATIC SPEECH RECOGNITION ANDKEYWORD…
mi.eng.cam.ac.uk/~ar527/ragni_icassp2017b.pdf22 Mar 2017: These weretrained on FLP data of 24 Babel languages and CTS data of 4 addi-tional languages, English, Spanish, Arabic and Mandarin, releasedby LDC. ... Stacked Hybrids were trained withand without stimulated training using monophone initialisation -
Automa(c Analysis of Mo(va(onal Interviewing with Diabetes Pa(ents…
mi.eng.cam.ac.uk/UKSpeech2017/posters/x_wei.pdf20 Nov 2017: Results:. Model Senone No. WER (%). Baseline DNN-‐HMM 3981 53.13. MI adapted DNN 3981 47.24. ... Hhit NF NREF PRC RCL. lium 35 144 93 0.24 0.38 ivector 42 159 93 0.26 0.45. -
MORPH-TO-WORD TRANSDUCTION FOR ACCURATE AND EFFICIENT AUTOMATICSPEECH …
mi.eng.cam.ac.uk/~ar527/ragni_icassp2017a.pdf22 Mar 2017: FLP Web FLP Web (#) ASR KWSSwahili 294 – 24.4 0 8.2 8.5 19.6Dholuo 467 1,217 17.5 18.8 6.1 3.0 10.0Amharic 388 ... 4, pp. 1738–1752, 1990. [24] P. Ghahremani, B. BabaAli, D. Povey, K. -
Future Word Contexts in Neural Network Language Models
mi.eng.cam.ac.uk/UKSpeech2017/posters/x_chen.pdf17 Nov 2017: dev evalwords (w/s) PPL. ng4 - - 80.4 23.8 24.2uni-rnn - 4.5K 66.8 21.7 22.1. ... ng4 - 23.8 23.5 24.2 23.9uni-rnn - 21.7 21.5 21.9 21.7. -
Experimental Studies on Teacher-student Training of Deep Neural…
mi.eng.cam.ac.uk/UKSpeech2017/posters/q_li.pdf20 Nov 2017: 22. 23. 24. 25. 26. 27. 28. 29. 30. PER. (%). 3-layer (100) Baseline3-layer (100) Student3-layer (250) Baseline3-layer (250) Student3-layer (500) Baseline3-layer (500) Student4-layer (500) ... PER (%)7-layer (500) 24.55 23.55RNN 23.84 20.59Ensemble 23.73 -
Use of Graphemic Lexicons for Spoken Language Assessment
mi.eng.cam.ac.uk/UKSpeech2017/posters/k_knill.pdf17 Nov 2017: Decoder Gujarati Mixed(word) %PER %GER %PER %GER. Ph 25.8 24.9 33.9 32.9Gr 29.0 23.7 36.6 30.8.
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