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Automatic Speech Recognition And Limited Vocabulary: A Survey

arXiv 2108.10254 ↗First posted 23 Aug 2021cs.AI cs.SD eess.AScited by 0

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Abstract (arXiv)

Automatic Speech Recognition (ASR) is an active field of research due to its large number of applications and the proliferation of interfaces or computing devices that can support speech processing. However, the bulk of applications are based on well-resourced languages that overshadow under-resourced ones. Yet, ASR represents an undeniable means to promote such languages, especially when designing human-to-human or human-to-machine systems involving illiterate people. An approach to design an ASR system targeting under-resourced languages is to start with a limited vocabulary. ASR using a limited vocabulary is a subset of the speech recognition problem that focuses on the recognition of a small number of words or sentences. This paper aims to provide a comprehensive view of mechanisms behind ASR systems as well as techniques, tools, projects, recent contributions, and possible future directions in ASR using a limited vocabulary. This work consequently provides a way forward when designing an ASR system using limited vocabulary. Although an emphasis is put on limited vocabulary, most of the tools and techniques reported in this survey can be applied to ASR systems in general.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 158 references that are in the map. First posted on arXiv 23 Aug 2021.

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23 of 158 references.

References · 23

  1. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  2. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  3. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 741
  4. 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
  5. 2019Direct speech-to-speech translation with a sequence-to-sequence modelcited by 118
  6. 2021Emotional Voice Conversion: Theory, Databases and ESDcited by 113
  7. 2019ASR is all you need: cross-modal distillation for lip readingcited by 85
  8. 2016Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classificationcited by 84
  9. 2019Machine Learning at the Network Edge: A Surveycited by 83
  10. 2019Jasper: An End-to-End Convolutional Neural Acoustic Modelcited by 75
  11. 2019Optimizing Speech Recognition For The Edgecited by 32
  12. 2020FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulationcited by 21
11 more
  1. 2019Biometrics Recognition Using Deep Learning: A Surveycited by 11
  2. 2019Predicting detection filters for small footprint open-vocabulary keyword spottingcited by 10
  3. 2017Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selectioncited by 9
  4. 2018Visual-Only Recognition of Normal, Whispered and Silent Speechcited by 7
  5. 2019Automatic Speech Recognition Services: Deaf and Hard-of-Hearing Usabilitycited by 5
  6. 2016Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devicescited by 4
  7. 2019Kurdish (Sorani) Speech to Text: Presenting an Experimental Datasetcited by 2
  8. 2019Audiogmenter: a MATLAB Toolbox for Audio Data Augmentationcited by 2
  9. 2015Rethinking Network Connectivity in Rural Communities in Camerooncited by 1
  10. 2016On model architecture for a children's speech recognition interactive dialog systemcited by 1
  11. 2019Practical Speech Recognition with HTKcited by 1

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