Speaker Adaptive Training for Speech Recognition Based on Attention-Over-Attention Mechanism

In our previous work, we introduced a speaker adaptive training method based on frame-level attention mechanism for speech recognition, which has been proved an effective way to do speaker adaptive training. In this paper, we present an improved method by introducing the attention-over-attention mechanism. This attention module is used to further measure the contribution of each frame to the speaker embeddings in an utterance, and then generate an utterance-level speaker embedding to perform speaker adaptive training. Compared with the frame-level ones, the generated utterance-level speaker embeddings are more representative and stable. Experiments on both the Switchboard and AISHELL-2 tasks show that our method can achieve a relative word error rate reduction of approximately 8.0% compared with the speaker independent model, and over 6.0% compared with the traditional utterance-level d-vector-based speaker adaptive training method.