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 …

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 …

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 …

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 …

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

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

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 …

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 …

In this work, we extend our previously proposed offline SpatialNet for long-term streaming
multichannel speech enhancement in both static and moving speaker scenarios. SpatialNet …

Abstract English language education is undergoing a transformative shift, propelled by
advancements in technology. This research explores the integration of the Internet of Things …