A Dropout-Based Single Model Committee Approach for Active Learning in ASR

Abstract

In this paper, we proposed a new committee-based approach for active learning (AL) in automatic speech recognition (ASR). This approach can achieve lower recognition word error rate (WER) with fewer transcription by selecting the most informative samples. Different from previous committee-based AL approaches, the committee construction process of this approach needs to train only one acoustic model(AM) with dropout. Since only one model needs to be trained, this approach is simpler and faster. At the same time, the AM will be improved continuously, we also found this approach is more robust to its improvement. In experiments, we compared our approach with the random sampling and another state-of-the-art committee-based approach: heterogeneous neural networks (HNN) based approach. We examined our approach in WER, the time to construct committee and the robustness of model improvement in the Mandarin ASR task with 1600 hours speech data. The results showed that it achieves 2–3 times relative WER reduction compare with the random sampling, and it only uses 75% the time to achieve close WER with HNN-based approach.