# End-to-End Mandarin Tone Classification with Short Term Context Information

arXiv: https://arxiv.org/abs/2104.05657 · HTML: https://mlsanity.com/paper/2104.05657/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances](https://mlsanity.com/paper/2002.06033/) (2020)
- [Tone Recognition Using Lifters and CTC](https://mlsanity.com/paper/1807.02465/) (2018)
- [Mandarin tone modeling using recurrent neural networks](https://mlsanity.com/paper/1711.01946/) (2017)

## Built on by (most cited first, 3 of 3)

- [Automatic recognition of suprasegmentals in speech](https://mlsanity.com/paper/2108.01122/) (2021)
- [A corpus-based investigation of pitch contours of monosyllabic words in conversational Taiwan Mandarin](https://mlsanity.com/paper/2409.07891/) (2024)
- [Tone recognition in low-resource languages of North-East India: peeling the layers of SSL-based speech models](https://mlsanity.com/paper/2506.03606/) (2025)

## Abstract (arXiv)

In this paper, we propose an end-to-end Mandarin tone classification method from continuous speech utterances utilizing both the spectrogram and the short-term context information as the input. Both spectrograms and context segment features are used to train the tone classifier. We first divide the spectrogram frames into syllable segments using force alignment results produced by an ASR model. Then we extract the short-term segment features to capture the context information across multiple syllables. Feeding both the spectrogram and the short-term context segment features into an end-to-end model could significantly improve the performance. Experiments are performed on a large-scale open-source Mandarin speech dataset to evaluate the proposed method. Results show that this method improves the classification accuracy from 79.5% to 92.6% on the AISHELL3 database.