Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition

面向端到端普通话语音识别的尖峰触发上下文偏置方法

Spike-Triggered Contextual Biasing for End-to-End Mandarin Speech Recognition

针对深度上下文偏置方法偏置程度难控的问题，提出支持显式隐式偏置的尖峰触发深度偏置方法，搭配采样增强与短语过滤改进，在WenetSpeech数据集上CER相对降32.0%，上下文短语降68.6%。

An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR

In the present paper, an attempt is made to combine Mask-CTC and the triggered attention mechanism to construct a streaming end-to-end automatic speech recognition (ASR) system that provides high performance with low latency. The triggered attention mechanism, which performs autoregressive decoding triggered by the CTC spike, has shown to be effective in streaming ASR. However, in order to maintain high accuracy of alignment estimation based on CTC outputs, which is the key to its performance, it is inevitable that decoding should be performed with some future information input (i.e., with hig…

Boundary and Context Aware Training for CIF-based Non-Autoregressive End-to-end ASR

Continuous integrate-and-fire (CIF) based models, which use a soft and monotonic alignment mechanism, have been well applied in non-autoregressive (NAR) speech recognition with competitive performance compared with other NAR methods. However, such an alignment learning strategy may suffer from an erroneous acoustic boundary estimation, severely hindering the convergence speed as well as the system performance. In this paper, we propose a boundary and context aware training approach for CIF based NAR models. Firstly, the connectionist temporal classification (CTC) spike information is utilized …

A CTC Triggered Siamese Network with Spatial-Temporal Dropout for Speech Recognition

Siamese networks have shown effective results in unsupervised visual representation learning. These models are designed to learn an invariant representation of two augmentations for one input by maximizing their similarity. In this paper, we propose an effective Siamese network to improve the robustness of End-to-End automatic speech recognition (ASR). We introduce spatial-temporal dropout to support a more violent disturbance for Siamese-ASR framework. Besides, we also relax the similarity regularization to maximize the similarities of distributions on the frames that connectionist temporal c…

基于Mask-CTC的编码器预训练在流式端到端语音识别中的应用

Mask-CTC-based Encoder Pre-training for Streaming End-to-End Speech Recognition

本文研究了基于Mask-CTC的编码器预训练方法在流式端到端ASR中的应用，验证其在不同模型架构（如Transformer-Transducer）上降低延迟、提升准确率及获得准确输出脉冲时序的有效性。