2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPE-22.4 | ||
| Paper Title | MULTI-DIALECT SPEECH RECOGNITION IN ENGLISH USING ATTENTION ON ENSEMBLE OF EXPERTS | ||
| Authors | Amit Das, Kshitiz Kumar, Jian Wu, Microsoft, United States | ||
| Session | SPE-22: Speech Recognition 8: Multilingual Speech Recognition | ||
| Location | Gather.Town | ||
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 | ||
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 | ||
| Presentation | Poster | ||
| Topic | Speech Processing: [SPE-MULT] Multilingual Recognition and Identification | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | In the presence of a wide variety of dialects, training dialect-specific models for each dialect is a demanding task. Previous studies have explored training a single model that is robust across multiple dialects. These studies have used either multi-condition training, multi-task learning, end-to-end modeling, or ensemble modeling. In this study, we further explore using a single model for multi-dialect speech recognition using ensemble modeling. First, we build an ensemble of dialect-specific models (or experts). Then we linearly combine the outputs of the experts using attention weights generated by a long short-term memory (LSTM) network. For comparison purposes, we train a model that jointly learns to recognize and classify dialects using multi-task learning and a second model using multi-condition training. We train all of these models with about 60,000 hours of speech data collected in American English, Canadian English, British English, and Australian English. Experimental results reveal that our best proposed model achieved an average 4.74% word error rate reduction (WERR) compared to the strong baseline model. | ||