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A learned emotion space for emotion recognition and emotive speech…
mi.eng.cam.ac.uk/UKSpeech2017/posters/z_hodari_poster.pdf23 Dec 2017: Table 1: Performance classifying; happy, sad, angry, neutral. Model Inputs AccuracyRandom N/A 24.14%Most common N/A 33.00%LSTM eGeMAPS LLDs 43.17%TD-CNN Spectrogram -
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. -
Low-Resource Speech Recognition and Keyword-Spotting
mi.eng.cam.ac.uk/~mjfg/SPECOM_2017.pdf29 Nov 2017: 23/63. Stimulated Systems. /ey//em/. /sil/. /sh/. /ow/ /ay/. 24/63. Stimulated Network Training. • -
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. -
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 -
Modular Construction of Complex Deep Learning Architectures in HTK
mi.eng.cam.ac.uk/UKSpeech2017/posters/f_kreyssig.pdf20 Nov 2017: I All models used 24 log-Mel filter bank coefficients with their and values as input features, except the CNN which used40 without any. -
.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. -
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. -
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%. -
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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