2. Advances in WP1 Chania Meeting – May 2007 www.loquendo.com

3. 2 Summary Test on Hiwire DB with denoising methods developed in the project: –Wiener SNR dep. Spectral Subtraction –Ephraim-Malah SNR dep. Spectral Attenuation Loquendo FE – UGR PEQ Integration –Details –Results on Hiwire db

4. HIWIRE DB Test Chania Meeting – May 2007 www.loquendo.com

5. 4 Test Conditions Test on the last 50 utterances of each speaker (50-99) The first 50 utterances of each speaker (0-50) left for development or adaptation Four noise conditions: –Clean –Low Noise (SNR = 10 dB) –Medium Noise (SNR = 5 dB) –High Noise (SNR = -5 dB) 4049 utterances for each condition, from 81 speakers of 4 nationalities

6. 5 HMM-ANN Models Two HMM-ANN models have been trained: Telephone 8 kHz: trained with a large telephone corpus (LDC Macrophone + SpeechDat Mobile) Microphone 16 kHz: trained with a collection of microphone corpora (timit, wsj0-1, vehic1us-ch0)

7. 6 Test Results Models Denoising method Noise ConditionAVG E.R. % CleanLNMNHN Telephone 8 kHz (Macrophone) No Den88.451.127.32.8 42.4 - WIE88.370.054.116.3 57.2 25.7 EM88.374.762.020.1 61.3 32.8 Microphone 16kHz (timit-wsj0-1- vehic1us) No Den90.549.127.55.0 43.0 - WIE90.468.551.114.5 56.2 23.2 EM90.271.955.016.6 58.4 27.0

8. 7 Test Results

9. 8 Comments on Results The 16 kHz models are more accurate on clean speech (90.5% vs. 88.4%) Ephraim-Malah noise reduction always outperforms Wiener spectral subtraction (32.8% vs. 25.7% and 25.7% vs. 21.8% E.R.).

10. Loquendo FE UGR PEQ integration Chania Meeting – May 2007 www.loquendo.com

11. 10 PEQ Integration (Loquendo & UGR) Loquendo FE UGR PEQ Loquendo ASR Denoise (Power Spectrum level) Feature Normalization (Frame -13 coeff- level) Phoneme-based Models

12. 11 PEQ effects

13. 12 PEQ Results ModelsDen. Norm. Noise ConditionAVG CleanLNMNHN wsj0 16 kHz NO 89.344.220.92.039.1 wsj0 16 kHz E.M.NO 89.269.653.715.457.0 wsj0 16 kHz NOPEQ 85.767.250.414.754.5 wsj0 16 kHz E.M.PEQ 85.273.759.519.859.5 The HMM-ANN models employed are: WSJ0 models WSJ0 models + E.M. denoising WSJ0 models + E.M. denoising + PEQ

14. 13 EM Denoise and PEQ

15. 14 Comments on EM denoising - PEQ On noisy speech (LN, MN, HN): –both EM denoising and PEQ obtain a good improvement –best results are obtained when adding the effects of EM de- noising and PEQ normalization. On clean speech: –EM denoising does not decrease performances –PEQ normalization slightly decreases performances PEQ is very useful in mismatched conditions can (slightly) decrease performances in matched conditions (e.g. clean speech)

16. 15 Test on TTS American Voice (Dave) ModelsDaveHiwire DB clean Telephone 8 kHz (Macrophone) 98.988.3 Micro 16 kHz (wsj0) 99.788.1 We have used the American voice DAVE of Loquendo TTS to read the 4049 sentences of the Hiwire DB The great difference in results is due to non-native pronounce Es. “Range Forty” pronounced by Dave by a French speaker by a Greek speaker

17. 16 WP1: Workplan Selection of suitable benchmark databases; (m6) Completion of LASR baseline experimentation of Spectral Subtraction (Wiener SNR dependent) (m12) Discriminative VAD (training+AURORA3 testing) (m16) Exprimentation of Spectral Attenuation rule (Ephraim-Malah SNR dependent) (m21) Preliminary results on spectral subtraction and HEQ techniques (m24) Integration of denoising and normalization techniques (PEQ) (m33)