Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models (L18-1)
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| Challenge: | Existing speaker adaptation algorithms for BLSTM-CTC AMs are lacking . TED-LIUM corpus shows speaker adaptation provides 11-20% word error rate reduction over baseline model built on raw filter-bank features. |
| Approach: | They propose to use feature-space adaptation techniques for bidirectional long short term memory (BLSTM) recurrent neural network based acoustic models trained with the connectionist temporal classification objective function to improve speaker adaptation. |
| Outcome: | The proposed approach provides up to 11-20% of word error reduction over baseline models on the TED-LIUM corpus. |
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