Multi-Head Decoder for End-to-End Speech Recognition
This work addresses speech recognition accuracy for applications like transcription, but it is incremental as it builds on existing multi-head attention models.
The paper tackles the problem of improving end-to-end speech recognition by proposing a multi-head decoder architecture that uses multiple decoders with different attention functions to capture varied speech/linguistic contexts, resulting in performance gains over conventional methods like location-based and multi-head attention models on the Corpus of Spontaneous Japanese.
This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model. In the multi-head attention model, multiple attentions are calculated, and then, they are integrated into a single attention. On the other hand, instead of the integration in the attention level, our proposed method uses multiple decoders for each attention and integrates their outputs to generate a final output. Furthermore, in order to make each head to capture the different modalities, different attention functions are used for each head, leading to the improvement of the recognition performance with an ensemble effect. To evaluate the effectiveness of our proposed method, we conduct an experimental evaluation using Corpus of Spontaneous Japanese. Experimental results demonstrate that our proposed method outperforms the conventional methods such as location-based and multi-head attention models, and that it can capture different speech/linguistic contexts within the attention-based encoder-decoder framework.