Synchronous Transformers for End-to-End Speech Recognition

Understand

For most of the attention-based sequence-to-sequence models, the decoder predicts the output sequence conditioned on the entire input sequence processed by the encoder.

• The asynchronous problem between the encoding and decoding makes these models difficult to be applied for online speech recognition.
• In this paper, we propose a model named synchronous transformer to address this problem, which can predict the output sequence chunk by chunk.
• Once a fixed-length chunk of the input sequence is processed by the encoder, the decoder begins to predict symbols immediately.