Automatic speech recognition using advanced deep learning approaches: A survey

H Kheddar, M Hemis, Y Himeur - Information Fusion, 2024 - Elsevier
Recent advancements in deep learning (DL) have posed a significant challenge for
automatic speech recognition (ASR). ASR relies on extensive training datasets, including …

Using transformers for multimodal emotion recognition: Taxonomies and state of the art review

S Hazmoune, F Bougamouza - Engineering Applications of Artificial …, 2024 - Elsevier
Emotion recognition is an aspect of human-computer interaction, affective computing, and
social robotics. Conventional unimodal approaches for emotion recognition, depending on …

End-to-end speech recognition: A survey

R Prabhavalkar, T Hori, TN Sainath… - … on Audio, Speech …, 2023 - ieeexplore.ieee.org
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 …

Transvg: End-to-end visual grounding with transformers

J Deng, Z Yang, T Chen, W Zhou… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
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: Convolution-augmented transformer for speech recognition

A Gulati, J Qin, CC Chiu, N Parmar, Y Zhang… - ar** real-time streaming transformer transducer for speech recognition on large-scale dataset
X Chen, Y Wu, Z Wang, S Liu… - ICASSP 2021-2021 IEEE …, 2021 - ieeexplore.ieee.org
Recently, Transformer based end-to-end models have achieved great success in many
areas including speech recognition. However, compared to LSTM models, the heavy …