mlsanityData to 7 Oct 2026

The History of Speech Recognition to the Year 2030

arXiv 2108.00084 ↗First posted 30 Jul 2021cs.CL cs.SD eess.AScited by 10

Not on the map. Why?

Abstract (arXiv)

The decade from 2010 to 2020 saw remarkable improvements in automatic speech recognition. Many people now use speech recognition on a daily basis, for example to perform voice search queries, send text messages, and interact with voice assistants like Amazon Alexa and Siri by Apple. Before 2010 most people rarely used speech recognition. Given the remarkable changes in the state of speech recognition over the previous decade, what can we expect over the coming decade? I attempt to forecast the state of speech recognition research and applications by the year 2030. While the changes to general speech recognition accuracy will not be as dramatic as in the previous decade, I suggest we have an exciting decade of progress in speech technology ahead of us.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 30 Jul 2021.

Builds on

22 of 37 references.

References · 22

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  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
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  5. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
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  10. 2015Deep Speech 2: End-to-End Speech Recognition in English and Mandarincited by 449
  11. 2015Attention-Based Models for Speech Recognitioncited by 400
  12. 2021GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audiocited by 391
10 more
  1. 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
  2. 2018Streaming End-to-end Speech Recognition For Mobile Devicescited by 230
  3. 2020Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognitioncited by 185
  4. 2019Self-Training for End-to-End Speech Recognitioncited by 128
  5. 2020Iterative Pseudo-Labeling for Speech Recognitioncited by 81
  6. 2016Achieving Human Parity in Conversational Speech Recognitioncited by 63
  7. 2019Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entitiescited by 41
  8. 2017English Conversational Telephone Speech Recognition by Humans and Machinescited by 40
  9. 2019An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Modelscited by 25
  10. 2020Differentiable Weighted Finite-State Transducerscited by 22

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 5; 2023: 3.

10 papers.

  1. 2021DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERTcited by 129
  2. 2022CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representationscited by 11
  3. 2022PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representationscited by 10
  4. 2023DQ-Whisper: Joint Distillation and Quantization for Efficient Multilingual Speech Recognitioncited by 10
  5. 2021DECAR: Deep Clustering for learning general-purpose Audio Representationscited by 7
  6. 2023A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videoscited by 5
  7. 2022data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setupcited by 4
  8. 2023Fast Certification of Vision-Language Models Using Incremental Randomized Smoothingcited by 4
  9. 2022Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognitioncited by 2
  10. 2022DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio Representation Learningcited by 0