# Tone Recognition Using Lifters and CTC

arXiv: https://arxiv.org/abs/1807.02465 · HTML: https://mlsanity.com/paper/1807.02465/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

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

- [Automatic recognition of suprasegmentals in speech](https://mlsanity.com/paper/2108.01122/) (2021)
- [End-to-End Mandarin Tone Classification with Short Term Context Information](https://mlsanity.com/paper/2104.05657/) (2021)
- [Unsupervised Learning and Representation of Mandarin Tonal Categories by a Generative CNN](https://mlsanity.com/paper/2509.17859/) (2025)

## Abstract (arXiv)

In this paper, we present a new method for recognizing tones in continuous speech for tonal languages. The method works by converting the speech signal to a cepstrogram, extracting a sequence of cepstral features using a convolutional neural network, and predicting the underlying sequence of tones using a connectionist temporal classification (CTC) network. The performance of the proposed method is evaluated on a freely available Mandarin Chinese speech corpus, AISHELL-1, and is shown to outperform the existing techniques in the literature in terms of tone error rate (TER).