Streaming End-to-End Speech Recognition with Joint CTC-Attention Based Models

Abstract

In this paper, we present a one-pass decoding algorithm for streaming recognition with joint connectionist temporal classification (CTC) and attention-based end-to-end automatic speech recognition (ASR) models. The decoding scheme is based on a frame-synchronous CTC prefix beam search algorithm and the recently proposed triggered attention concept. To achieve a fully streaming end-to-end ASR system, the CTC-triggered attention decoder is combined with a unidirectional encoder neural network based on parallel time-delayed long short-term memory (PTDLSTM) streams, which has demonstrated superior performance compared to various other streaming encoder architectures in earlier work. A new type of pre-training method is studied to further improve our streaming ASR models by adding residual connections to the encoder neural network and layer-wise removing them during the training process. The proposed joint CTC-triggered attention decoding algorithm, which enables streaming recognition of attention-based ASR systems, achieves similar ASR results compared to offline CTC-attention decoding and significantly better results compared to CTC prefix beam search decoding alone.