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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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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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Abstract:
In this paper, we explore several new schemes to train a seq2seq model to integrate a pre-trained LM. Our proposed fusion methods focus on the memory cell state and the hidden state in the seq2seq decoder long short-term memory (LSTM), and the memory cell state is updated by the LM unlike the prior studies. This means the memory retained by the main seq2seq would be adjusted by the external LM. These fusion methods have several variants depending on the architecture of this memory cell update and the use of memory cell and hidden states which directly affects the final label inference. We performed the experiments to show the effectiveness of the proposed methods in a mono-lingual ASR setup on the Librispeech corpus and in a transfer learning setup from a multilingual ASR (MLASR) base model to a low-resourced language. In Librispeech, our best model improved WER by 3.7%, 2.4% for test clean, test other relatively to the shallow fusion baseline, with multi-level decoding. In transfer learning from an MLASR ... : 4 pages, 1 figure, 5 tables, submitted to ICASSP 2019 ...
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Keyword:
Audio and Speech Processing eess.AS; 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.1811.02162 https://arxiv.org/abs/1811.02162
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