Results 41 to 50 of about 48,773 (160)

Streaming End-to-End Multi-Talker Speech Recognition [PDF]

IEEE Signal Processing Letters, 2021
5 pages, 3 figures.
Liang Lu 0001, Naoyuki Kanda, Jinyu Li 0001, Yifan Gong 0001   +3 more
openaire   +3 more sources

LWMD: A Comprehensive Compression Platform for End-to-End Automatic Speech Recognition Models

Applied Sciences, 2023
Recently end-to-end (E2E) automatic speech recognition (ASR) models have achieved promising performance. However, existing models tend to adopt increasing model sizes and suffer from expensive resource consumption for real-world applications. To compress
Yukun Liu, Ta Li, Pengyuan Zhang, Yonghong Yan   +3 more
doaj   +1 more source

Improving Transformer Based End-to-End Code-Switching Speech Recognition Using Language Identification

Applied Sciences, 2021
A Recurrent Neural Networks (RNN) based attention model has been used in code-switching speech recognition (CSSR). However, due to the sequential computation constraint of RNN, there are stronger short-range dependencies and weaker long-range ...
Zheying Huang, Pei Wang, Jian Wang, Haoran Miao, Ji Xu, Pengyuan Zhang   +5 more
doaj   +1 more source

Multi-Stream End-to-End Speech Recognition

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020
submitted to IEEE TASLP (In review).
Ruizhi Li, Xiaofei Wang 0007, Sri Harish Mallidi, Shinji Watanabe 0001, Takaaki Hori, Hynek Hermansky   +5 more
openaire   +3 more sources

End-to-End Speech Recognition Sequence Training With Reinforcement Learning

IEEE Access, 2019
End-to-end sequence modeling has become a popular choice for automatic speech recognition (ASR) because of the simpler pipeline compared to the conventional system and its excellent performance.
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura   +2 more
doaj   +1 more source

Ubranch Conformer: Integrating Up-Down Sampling and Branch Attention for Speech Recognition

IEEE Access
Conformer has become one of the most popular models in the field of automatic speech recognition, achieving superior speech recognition performance by integrating a convolutional module into Transformer. However, existing Conformer models face challenges
Yang Yang, Feng Yao, Mingke Liao, Yucheng Huang   +3 more
doaj   +1 more source

JSUM: A Multitask Learning Speech Recognition Model for Jointly Supervised and Unsupervised Learning

Applied Sciences, 2023
In recent years, the end-to-end speech recognition model has emerged as a popular alternative to the traditional Deep Neural Network—Hidden Markov Model (DNN-HMM).
Nurmemet Yolwas, Weijing Meng
doaj   +1 more source

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Entropy, 2022
Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer ...
Mengzhuo Liu, Yangjie Wei
doaj   +1 more source

Segment boundary detection directed attention for online end-to-end speech recognition

EURASIP Journal on Audio, Speech, and Music Processing, 2020
Attention-based encoder-decoder models have recently shown competitive performance for automatic speech recognition (ASR) compared to conventional ASR systems.
Junfeng Hou, Wu Guo, Yan Song, Li-Rong Dai   +3 more
doaj   +1 more source

Attention-based latent features for jointly trained end-to-end automatic speech recognition with modified speech enhancement

Journal of King Saud University: Computer and Information Sciences, 2023
In this paper, we propose a joint training framework that efficiently combines time-domain speech enhancement (SE) with an end-to-end (E2E) automatic speech recognition (ASR) system utilizing attention-based latent features.
Da-Hee Yang, Joon-Hyuk Chang
doaj   +1 more source