Automated Tone Transcription and Clustering with Tone2Vec

TL;DR

This paper introduces Tone2Vec, a novel pitch-based representation for automatic tone transcription and clustering in tonal languages, significantly aiding linguistic fieldwork and analysis of endangered Sino-Tibetan dialects.

Contribution

We propose Tone2Vec, the first automatic tone transcription and clustering method using a new representation transformation, integrated into an accessible open-source toolkit.

Findings

Tone2Vec effectively captures fine-grained tone variation.

Our methods outperform existing approaches in dialect clustering.

The open-source package ToneLab facilitates automated tonal language analysis.

Abstract

Lexical tones play a crucial role in Sino-Tibetan languages. However, current phonetic fieldwork relies on manual effort, resulting in substantial time and financial costs. This is especially challenging for the numerous endangered languages that are rapidly disappearing, often compounded by limited funding. In this paper, we introduce pitch-based similarity representations for tone transcription, named Tone2Vec. Experiments on dialect clustering and variance show that Tone2Vec effectively captures fine-grained tone variation. Utilizing Tone2Vec, we develop the first automatic approach for tone transcription and clustering by presenting a novel representation transformation for transcriptions. Additionally, these algorithms are systematically integrated into an open-sourced and easy-to-use package, ToneLab, which facilitates automated fieldwork and cross-regional, cross-lexical analysis for tonal languages. Extensive experiments were conducted to demonstrate the effectiveness of our methods.