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Advances in WP2

Trento Meeting – January 2007. Advances in WP2. www.loquendo.com. Activities on WP2 since last meeting. Focus on WP1 (PEQ), WP3 (mobile platform) and WP4 (assessment) Test of adaptation on a project corpus: Hiwire Noisy Non-Native Corpus. Hiwire Noisy Corpus.

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Advances in WP2

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  1. Trento Meeting – January 2007 Advances in WP2 www.loquendo.com

  2. Activities on WP2 since last meeting • Focus on WP1 (PEQ), WP3 (mobile platform) and WP4 (assessment) • Test of adaptation on a project corpus: • Hiwire Noisy Non-Native Corpus

  3. Hiwire Noisy Corpus • Recorded in cockpit simulator with two noise levels • Microphone Array + Beamforming (ITC) • 5 non-native speakers. • Each speaker has pronounced 1 list of 100 sentences. • Sentences from the Hiwire Fixed-Demo grammar

  4. Experimental conditions • Starting models: • standard Loquendo ASR EN-US • Telephone models (8 kHz) • Training set: LDC Macrophone • Adaptation: first 50 utterances of each speaker • Test: last 50 utterances of each speaker • LM: Hiwire grammar (134 words voc.) • Signal proc.: down-sampling to 8 kHz

  5. Results on Hiwire Noisy corpus (High noise ) • Recognition model: ANN/HMM • Adaptation Model: LIN - LHN

  6. Results on Hiwire Noisy corpus (Low noise ) • Recognition model: ANN/HMM • Adaptation Model: LIN - LHN

  7. Discussion • In the case of Hiwire Noisy DB there are 3 main problems: • Noise level; • Non-Native Speakers • Channel: far-field microphone array + beamforming • If the WA of the default models is too low (~20-30%) adaptation is unable to improve because too many segmentation errors are present in the adaptation material • If the WA of the default models is acceptable (> 40%) adaptation can improve performances • On this corpus, where the channel + noise component is preponderant, LIN is in some cases better than LHN • The combination LIN+LHN is always better that the single techniques

  8. Workplan • Selection of suitable benchmark databases (m6) • Baseline set-up for the selected databases (m8) • LIN adaptation method implemented and experimented on the benchmarks (m12) • Experimental results on Hiwire database with LIN (m18) • Innovative NN adaptation methods and algorithms for acoustic modeling and experimental results (m21) • Further advances on new adaptation methods (m24) • Unsupervised Adaptation: algorithms and experimentation (m33)

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