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Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization ...
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Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation ...
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Self-Guided Curriculum Learning for Neural Machine Translation ...
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Arabic Speech Recognition by End-to-End, Modular Systems and Human ...
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Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yoloxóchitl Mixtec ...
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Leveraging Pre-trained Language Model for Speech Sentiment Analysis ...
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End-to-end ASR to jointly predict transcriptions and linguistic annotations ...
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Differentiable Allophone Graphs for Language-Universal Speech Recognition ...
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Speech Representation Learning Combining Conformer CPC with Deep Cluster for the ZeroSpeech Challenge 2021 ...
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CHiME-6 Challenge: Tackling multispeaker speech recognition for unsegmented recordings
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In: CHiME 2020 - 6th International Workshop on Speech Processing in Everyday Environments ; https://hal.inria.fr/hal-02546993 ; CHiME 2020 - 6th International Workshop on Speech Processing in Everyday Environments, May 2020, Barcelona / Virtual, Spain (2020)
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A Comparative Study on Transformer vs RNN in Speech Applications ...
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Karita, Shigeki; Chen, Nanxin; Hayashi, Tomoki; Hori, Takaaki; Inaguma, Hirofumi; Jiang, Ziyan; Someki, Masao; Soplin, Nelson Enrique Yalta; Yamamoto, Ryuichi; Wang, Xiaofei; Watanabe, Shinji; Yoshimura, Takenori; Zhang, Wangyou. - : arXiv, 2019
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Abstract:
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called Transformer, which achieves state-of-the-art performance in neural machine translation and other natural language processing applications. We undertook intensive studies in which we experimentally compared and analyzed Transformer and conventional recurrent neural networks (RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS benchmarks. Our experiments revealed various training tips and significant performance benefits obtained with Transformer for each task including the surprising superiority of Transformer in 13/15 ASR benchmarks in comparison with RNN. We are preparing to release Kaldi-style reproducible recipes using open source and publicly available datasets for all the ASR, ST, and TTS tasks for the community to succeed our exciting ... : Accepted at ASRU 2019 ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.1909.06317 https://arxiv.org/abs/1909.06317
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Towards Online End-to-end Transformer Automatic Speech Recognition ...
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The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
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In: Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-01744021 ; Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association, Sep 2018, Hyderabad, India (2018)
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Analysis of Multilingual Sequence-to-Sequence speech recognition systems ...
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Language model integration based on memory control for sequence to sequence speech recognition ...
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