The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

This paper proposes an efficient memory transformer Emformer for low latency streaming
speech recognition. In Emformer, the long-range history context is distilled into an …

Attention is a very popular and effective mechanism in artificial neural network-based
sequence-to-sequence models. In this survey paper, a comprehensive review of the different …

Today, the Transformer model, which allows parallelization and also has its own internal
attention, has been widely used in the field of speech recognition. The great advantage of …

Self-attention (SA) is a critical component of Transformer neural networks that have
succeeded in automatic speech recognition (ASR). In this paper, we analyze the role of SA …

Nowadays, most methods for end-to-end contextual speech recognition bias the recognition
process towards contextual knowledge. Since all-neural contextual biasing methods rely on …

Transformer-based acoustic modeling has achieved great suc-cess for both hybrid and
sequence-to-sequence speech recogni-tion. However, it requires access to the full …

Speech Emotion Recognition (SER) refers to the recognition of human emotions from
natural speech. If done accurately, it can offer a number of benefits in building human …

Transformer 作为一种新的深度学习算法框架, 得到了越来越多研究人员的关注,
成为目前的研究热点. Transformer 模型中的自注意力机制受人类只关注于重要事物的启发 …

With the growth of the Internet of Things and the rise of Big Data, data processing and
machine learning applications are being moved to cheap and low size, weight, and power …