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A study of transformer-based end-to-end speech recognition system for Kazakh language [PDF]
Scientific Reports, 2022 Today, the Transformer model, which allows parallelization and also has its own internal attention, has been widely used in the field of speech recognition.
Mamyrbayev Orken +4 more
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End-to-End Audiovisual Speech Recognition [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Several end-to-end deep learning approaches have been recently presented which extract either audio or visual features from the input images or audio signals and perform speech recognition. However, research on end-to-end audiovisual models is very limited.
Stavros Petridis +5 more
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End-to-End Speech-to-Dialog-Act Recognition [PDF]
Interspeech 2020, 2020 Spoken language understanding, which extracts intents and/or semantic concepts in utterances, is conventionally formulated as a post-processing of automatic speech recognition. It is usually trained with oracle transcripts, but needs to deal with errors by ASR.
Viet-Trung Dang +4 more
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Arabic speech recognition using end‐to‐end deep learning
IET Signal Processing, 2021 Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be integrated with other systems better than Arabic ASR methods without diacritics.
Hamzah A. Alsayadi +3 more
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End-to-end Multimodal Speech Recognition [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 5 pages, 5 figures, Accepted at IEEE International Conference on Acoustics, Speech and Signal Processing 2018 (ICASSP 2018)
Shruti Palaskar +2 more
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Synchronous Transformers for end-to-end Speech Recognition [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 For most of the attention-based sequence-to-sequence models, the decoder predicts the output sequence conditioned on the entire input sequence processed by the encoder. The asynchronous problem between the encoding and decoding makes these models difficult to be applied for online speech recognition.
Zhengkun Tian +5 more
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End-to-end Anchored Speech Recognition [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Voice-controlled house-hold devices, like Amazon Echo or Google Home, face the problem of performing speech recognition of device-directed speech in the presence of interfering background speech, i.e., background noise and interfering speech from another person or media device in proximity need to be ignored.
Yiming Wang 0006 +5 more
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Towards End-to-End Unsupervised Speech Recognition
2022 IEEE Spoken Language Technology Workshop (SLT), 2023 Preprint
Alexander H. Liu +3 more
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Self-Training for End-to-End Speech Recognition [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 We revisit self-training in the context of end-to-end speech recognition. We demonstrate that training with pseudo-labels can substantially improve the accuracy of a baseline model. Key to our approach are a strong baseline acoustic and language model used to generate the pseudo-labels, filtering mechanisms tailored to common errors from sequence-to ...
Jacob Kahn, Ann Lee 0001, Awni Y. Hannun
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Research Status and Prospect of Transformer in Speech Recognition
Jisuanji kexue yu tansuo, 2021 As a new deep learning algorithm framework, Transformer has attracted more and more researchers?? attention and has become a current research hotspot. Inspired by humans focusing on important things only, the self-attention mechanism in the Transformer ...
ZHANG Xiaoxu, MA Zhiqiang, LIU Zhiqiang, ZHU Fangyuan, WANG Chunyu
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