# Automatic Speech Recognition And Limited Vocabulary: A Survey

arXiv: https://arxiv.org/abs/2108.10254 · HTML: https://mlsanity.com/paper/2108.10254/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 158 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 23 of 23)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Direct speech-to-speech translation with a sequence-to-sequence model](https://mlsanity.com/paper/1904.06037/) (2019)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [ASR is all you need: cross-modal distillation for lip reading](https://mlsanity.com/paper/1911.12747/) (2019)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Jasper: An End-to-End Convolutional Neural Acoustic Model](https://mlsanity.com/paper/1904.03288/) (2019)
- [Optimizing Speech Recognition For The Edge](https://mlsanity.com/paper/1909.12408/) (2019)
- [FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation](https://mlsanity.com/paper/2011.05731/) (2020)
- [Biometrics Recognition Using Deep Learning: A Survey](https://mlsanity.com/paper/1912.00271/) (2019)
- [Predicting detection filters for small footprint open-vocabulary keyword spotting](https://mlsanity.com/paper/1912.07575/) (2019)
- [Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection](https://mlsanity.com/paper/1711.03689/) (2017)
- [Visual-Only Recognition of Normal, Whispered and Silent Speech](https://mlsanity.com/paper/1802.06399/) (2018)
- [Automatic Speech Recognition Services: Deaf and Hard-of-Hearing Usability](https://mlsanity.com/paper/1909.02853/) (2019)
- [Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices](https://mlsanity.com/paper/1606.06061/) (2016)
- [Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset](https://mlsanity.com/paper/1911.13087/) (2019)
- [Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation](https://mlsanity.com/paper/1912.05472/) (2019)
- [Rethinking Network Connectivity in Rural Communities in Cameroon](https://mlsanity.com/paper/1505.04449/) (2015)
- [On model architecture for a children's speech recognition interactive dialog system](https://mlsanity.com/paper/1605.07733/) (2016)
- [Practical Speech Recognition with HTK](https://mlsanity.com/paper/1908.02119/) (2019)

## 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.