mlsanityData to 7 Oct 2026

Automatic recognition of suprasegmentals in speech

arXiv 2108.01122 ↗First posted 2 Aug 2021cs.CLcited by 9

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Abstract (arXiv)

This study reports our efforts to improve automatic recognition of suprasegmentals by fine-tuning wav2vec 2.0 with CTC, a method that has been successful in automatic speech recognition. We demonstrate that the method can improve the state-of-the-art on automatic recognition of syllables, tones, and pitch accents. Utilizing segmental information, by employing tonal finals or tonal syllables as recognition units, can significantly improve Mandarin tone recognition. Language models are helpful when tonal syllables are used as recognition units, but not helpful when tones are recognition units. Finally, Mandarin tone recognition can benefit from English phoneme recognition by combining the two tasks in fine-tuning wav2vec 2.0.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 10 of its 40 references that are in the map. First posted on arXiv 2 Aug 2021.

Builds on

10 of 40 references.

References · 10

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2017AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baselinecited by 364
  4. 2020AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselinescited by 111
  5. 2020Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languagescited by 30
  6. 2020The role of context in neural pitch accent detection in Englishcited by 5
  7. 2018Tone Recognition Using Lifters and CTCcited by 3
  8. 2021End-to-End Mandarin Tone Classification with Short Term Context Informationcited by 3
  9. 2018Effects of Word Embeddings on Neural Network-based Pitch Accent Detectioncited by 2
  10. 2021An Optimized Signal Processing Pipeline for Syllable Detection and Speech Rate Estimationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 3; 2025: 4; 2026: 1.

9 papers.

  1. 2024Encoding of lexical tone in self-supervised models of spoken languagecited by 22
  2. 2024A layer-wise analysis of Mandarin and English suprasegmentals in SSL speech modelscited by 18
  3. 2025Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learningcited by 4
  4. 2024Automated Tone Transcription and Clustering with Tone2Veccited by 2
  5. 2021Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognitioncited by 1
  6. 2025Normalization through Fine-tuning: Understanding Wav2vec 2.0 Embeddings for Phonetic Analysiscited by 1
  7. 2025Unsupervised Learning and Representation of Mandarin Tonal Categories by a Generative CNNcited by 1
  8. 2025The Tonogenesis Continuum in Tibetan: A Computational Investigationcited by 0
  9. 2026ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classificationcited by 0