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Semi-supervised cycle-consistency training for end-to-end ASR using unpaired speech
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Enforcing constraints for multi-lingual and cross-lingual speech-to-text systems
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Knowledge base integration in biomedical natural language processing applications
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Learning speech embeddings for speaker adaptation and speech understanding
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Modeling phones, keywords, topics and intents in spoken languages
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Speech technology for unwritten languages
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In: ISSN: 2329-9290 ; EISSN: 2329-9304 ; IEEE/ACM Transactions on Audio, Speech and Language Processing ; https://hal.inria.fr/hal-02480675 ; IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TASLP.2020.2973896⟩ (2020)
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How Phonotactics Affect Multilingual and Zero-shot ASR Performance ...
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That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages ...
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Autosegmental Neural Nets: Should Phones and Tones be Synchronous or Asynchronous? ...
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Identify Speakers in Cocktail Parties with End-to-End Attention ...
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Acoustic event, spoken keyword and emotional outburst detection
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Automatic speech recognition for low-resource languages and dialects
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The benefits of acoustic perceptual information for speech processing systems
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Abstract:
The frame-synchronized framework has dominated many speech processing systems, such as ASR and AED targeting human speech activities. These systems have little consideration for the science behind speech and treat the task as a simple statistical classification. The framework also assumes each feature vector to be equally important to the task. However, through some preliminary experiments, this study has found evidence that some concepts defined in speech perception theories such as auditory roughness and acoustic landmarks can act as heuristics to these systems and benefit them in multiple ways. Findings of acoustic landmarks hint that the idea of treating each frame equally might not be optimal. In some cases, landmark information can improve system accuracy through highlighting the more significant frames, or improve the acoustic model accuracy by training through MTL. Further investigation into the topic found experimental evidence suggesting that acoustic landmark information can also benefit end-to-end acoustic models trained through CTC loss. With the help of acoustic landmarks, CTC models can converge with less training data and achieve lower error rate. For the first time, positive results were collected on a mid-size ASR corpus (WSJ) for acoustic landmarks. The results indicate that audio perception information can benefit a broad range of audio processing systems.
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Keyword:
Acoustic Landmark; AED; ASR; Auditory Roughness; CTC; FPGA; IoT; MTL
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URL: http://hdl.handle.net/2142/104888
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Dealing with linguistic mismatches for automatic speech recognition
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Bayesian models for unit discovery on a very low resource language
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In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; https://hal.archives-ouvertes.fr/hal-01709589 ; IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, Alberta, Canada (2018)
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Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the “Speaking rosetta” JSALT 2017 workshop
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In: ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-01709578 ; ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Alberta, Canada (2018)
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Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop ...
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