Results 51 to 60 of about 48,773 (160)

A Light-Weight Autoregressive CNN-Based Frame Level Transducer Decoder for End-to-End ASR

Applied Sciences
A convolutional neural network (CNN) transducer decoder was proposed to reduce the decoding time of an end-to-end automatic speech recognition (ASR) system while maintaining accuracy.
Hyeon-Kyu Noh, Hong-June Park
doaj   +1 more source

End-to-End Training of a Large Vocabulary End-to-End Speech Recognition System [PDF]

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2019
In this paper, we present an end-to-end training framework for building state-of-the-art end-to-end speech recognition systems. Our training system utilizes a cluster of Central Processing Units(CPUs) and Graphics Processing Units (GPUs). The entire data reading, large scale data augmentation, neural network parameter updates are all performed "on-the ...
Chanwoo Kim 0001, Minkyoo Shin, Shatrughan Singh, Larry Heck, Dhananjaya Gowda, Sungsoo Kim, Kwangyoun Kim, Mehul Kumar, Jiyeon Kim, Kyungmin Lee, Changwoo Han, Abhinav Garg, Eunhyang Kim   +12 more
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Acoustic Model Fusion For End-to-End Speech Recognition

2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2023
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level. The E2E systems implicitly model all conventional ASR components, such as the acoustic model (AM) and the language model (LM), in a single network trained on audio-text pairs. Despite this simpler
Zhihong Lei, Mingbin Xu, Shiyi Han, Leo Liu, Zhen Huang 0001, Tim Ng, Yuanyuan Zhang, Ernest Pusateri, Mirko Hannemann, Yaqiao Deng, Man-Hung Siu   +10 more
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End-To-End Multi-Speaker Speech Recognition With Transformer [PDF]

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Recently, fully recurrent neural network (RNN) based end-to-end models have been proven to be effective for multi-speaker speech recognition in both the single-channel and multi-channel scenarios. In this work, we explore the use of Transformer models for these tasks by focusing on two aspects.
Xuankai Chang, Wangyou Zhang, Yanmin Qian, Jonathan Le Roux, Shinji Watanabe 0001   +4 more
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Comparative Study on End-to-End Speech Recognition Using Pre-trained Models [PDF]

Fayoum University Journal of Engineering
In the field of speech and audio signal processing, pre-trained models (PTMs) are commonly available. Pre-trained models (PTMs) offer a collection of initial weights and biases that may be adjusted for a particular task, which makes them a popular ...
Martha Ghobrial, Amr Gody, Sayed Muhammad   +2 more
doaj   +1 more source

Deep Context: End-to-end Contextual Speech Recognition [PDF]

2018 IEEE Spoken Language Technology Workshop (SLT), 2018
In automatic speech recognition (ASR) what a user says depends on the particular context she is in. Typically, this context is represented as a set of word n-grams. In this work, we present a novel, all-neural, end-to-end (E2E) ASR sys- tem that utilizes such context.
Golan Pundak, Tara N. Sainath, Rohit Prabhavalkar, Anjuli Kannan, Ding Zhao   +4 more
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Streaming End-to-end Speech Recognition for Mobile Devices [PDF]

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use ...
Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar, Ian McGraw, Raziel Alvarez, Ding Zhao, David Rybach, Anjuli Kannan, Yonghui Wu, Ruoming Pang, Qiao Liang 0001, Deepti Bhatia, Yuan Shangguan, Bo Li 0028, Golan Pundak, Khe Chai Sim, Tom Bagby, Shuo-Yiin Chang, Kanishka Rao, Alexander Gruenstein   +19 more
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A Spelling Correction Model for End-to-end Speech Recognition [PDF]

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs. Thus, the language model component of the end-to-end model is only trained on transcribed audio-text pairs, which leads to performance ...
Jinxi Guo, Tara N. Sainath, Ron J. Weiss
openaire   +2 more sources

A Novel Syllable-Level Signal Encryption for Robust Secure Speech Communication System

IEEE Access
Speech communication is vital for conveying information and emotions, yet it faces significant security threats. This research presents a novel signal encryption system that operates at the syllable level, preserving the natural flow of speech while ...
Albertus Anugerah Pekerti, Dessi Puji Lestari, Adi Indrayanto, Arif Sasongko   +3 more
doaj   +1 more source

Dialog-Context Aware end-to-end Speech Recognition [PDF]

2018 IEEE Spoken Language Technology Workshop (SLT), 2018
Existing speech recognition systems are typically built at the sentence level, although it is known that dialog context, e.g. higher-level knowledge that spans across sentences or speakers, can help the processing of long conversations. The recent progress in end-to-end speech recognition systems promises to integrate all available information (e.g ...
Suyoun Kim, Florian Metze
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