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Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese

https://doi.org/10.21437/interspeech.2018-1107
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11939993_73openalex-snapshot
HKUST/MTS: A Very Large Scale Mandarin Telephone Speech Corpus
inferredcites →https://doi.org/10.1109/89.917681openalex-snapshot
Syllable-based large vocabulary continuous speech recognition
inferredcites →https://doi.org/10.1109/asru.2017.8268932openalex-snapshot
Syllable-based acoustic modeling with CTC-SMBR-LSTM
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/icassp.2016.7472618openalex-snapshot
End-to-end attention-based large vocabulary speech recognition
inferredcites →https://doi.org/10.1109/icassp.2017.7953077openalex-snapshot
Very deep convolutional networks for end-to-end speech recognition
inferredcites →https://doi.org/10.1109/icassp.2018.8462682openalex-snapshot
An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model
inferredcites →https://doi.org/10.21437/interspeech.2007-481openalex-snapshot
Context dependent syllable acoustic model for continuous Chinese speech recognition
inferredcites →https://doi.org/10.21437/interspeech.2015-350openalex-snapshot
Fast and accurate recurrent neural network acoustic models for speech recognition
inferredcites →https://doi.org/10.21437/interspeech.2016-334openalex-snapshot
On Online Attention-Based Speech Recognition and Joint Mandarin Character-Pinyin Training
inferredcites →https://doi.org/10.21437/interspeech.2016-677openalex-snapshot
Multidimensional Residual Learning Based on Recurrent Neural Networks for Acoustic Modeling
inferredcites →https://doi.org/10.21437/interspeech.2016-71openalex-snapshot
Maximum a posteriori Based Decoding for CTC Acoustic Models
inferredcites →https://doi.org/10.21437/interspeech.2017-1296openalex-snapshot
Advances in Joint CTC-Attention Based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
inferredcites →https://doi.org/10.21437/interspeech.2017-232openalex-snapshot
An Analysis of “Attention” in Sequence-to-Sequence Models
inferredcites →https://doi.org/10.21437/interspeech.2017-233openalex-snapshot
A Comparison of Sequence-to-Sequence Models for Speech Recognition
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1506.07503openalex-snapshot
Attention-Based Models for Speech Recognition
inferredcites →https://doi.org/10.48550/arxiv.1508.01211openalex-snapshot
Listen, Attend and Spell
inferredcites →https://doi.org/10.48550/arxiv.1603.04467openalex-snapshot
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Dvsa: A Focused and Efficient Sparse Attention Via Explicit Selection for Speech Recognition
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Attention Rescoring with Transition Probabilities for End-to-End Mandarin Speech Recognition
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Semantically Corrected Amharic Automatic Speech Recognition
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Automatic Post-editing of Speech Recognition System Output Using Large Language Models
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Monotonic Gaussian regularization of attention for robust automatic speech recognition
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Chinese speech recognition based on improved end-to-end transformer learning model
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Suitability of syllable-based modeling units for end-to-end speech recognition in Sanskrit and other Indian languages
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Effective Exploitation of Posterior Information for Attention-Based Speech Recognition
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Acoustic Modeling Based on Deep Learning for Low-Resource Speech Recognition: An Overview
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