Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

In this paper, we present a neat yet effective transformer-based framework for visual
grounding, namely TransVG, to address the task of grounding a language query to the …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Recently, Transformer based end-to-end models have achieved great success in many
areas including speech recognition. However, compared to LSTM models, the heavy …

Conformer, combining convolution and self-attention sequentially to capture both local and
global information, has shown remarkable performance and is currently regarded as the …

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 …

A huge amount of research has been done in the field of speech signal processing in recent
years. In particular, there has been increasing interest in the automatic speech recognition …

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as
quickly and accurately as possible, while full-context ASR waits for the completion of a full …

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 …