# Automatic recognition of suprasegmentals in speech

arXiv: https://arxiv.org/abs/2108.01122 · HTML: https://mlsanity.com/paper/2108.01122/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages](https://mlsanity.com/paper/2012.12121/) (2020)
- [The role of context in neural pitch accent detection in English](https://mlsanity.com/paper/2004.14846/) (2020)
- [Tone Recognition Using Lifters and CTC](https://mlsanity.com/paper/1807.02465/) (2018)
- [End-to-End Mandarin Tone Classification with Short Term Context Information](https://mlsanity.com/paper/2104.05657/) (2021)
- [Effects of Word Embeddings on Neural Network-based Pitch Accent Detection](https://mlsanity.com/paper/1805.05237/) (2018)
- [An Optimized Signal Processing Pipeline for Syllable Detection and Speech Rate Estimation](https://mlsanity.com/paper/2103.04346/) (2021)

## Built on by (most cited first, 9 of 9)

- [Encoding of lexical tone in self-supervised models of spoken language](https://mlsanity.com/paper/2403.16865/) (2024)
- [A layer-wise analysis of Mandarin and English suprasegmentals in SSL speech models](https://mlsanity.com/paper/2408.13678/) (2024)
- [Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learning](https://mlsanity.com/paper/2506.02584/) (2025)
- [Automated Tone Transcription and Clustering with Tone2Vec](https://mlsanity.com/paper/2410.02324/) (2024)
- [Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition](https://mlsanity.com/paper/2112.12522/) (2021)
- [Normalization through Fine-tuning: Understanding Wav2vec 2.0 Embeddings for Phonetic Analysis](https://mlsanity.com/paper/2503.04814/) (2025)
- [Unsupervised Learning and Representation of Mandarin Tonal Categories by a Generative CNN](https://mlsanity.com/paper/2509.17859/) (2025)
- [The Tonogenesis Continuum in Tibetan: A Computational Investigation](https://mlsanity.com/paper/2510.22485/) (2025)
- [ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification](https://mlsanity.com/paper/2609.24903/) (2026)

## 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.