In this paper, we introduce an unsupervised approach for Speech Segmentation, which builds on previously researched approaches, e.g., Speaker Diarization, while being applicable to an inclusive set of acoustic-semantic distinctions, paving a path towards a general Unsupervised Speech Segmentation approach. Unlike traditional speech and audio segmentation, which mainly focuses on spectral changes in the input signal, e.g., phone segmentation, our approach tries to segment the spoken utterance into chunks with differing acoustic-semantic styles, focusing on acoustic-semantic information that does not translate well into text, e.g., emotion or speaker. While most Speech Segmentation tasks only handle one style change, e.g., emotion diarization, our approach tries to handle multiple acoustic-semantic style changes. Leveraging recent advances in Speech Language Models (SLMs), we propose a simple unsupervised method to segment a given speech utterance. We empirically demonstrate the effectiveness of the proposed approach by considering several setups. Results suggest that the proposed method is superior to the evaluated baselines on boundary detection, segment purity, and over-segmentation. Code is available at https://github.com/avishaiElmakies/unsupervised_speech_segmentation_using_slm.

本文提出了一种无监督的语音分割方法，旨在解决传统语音分割方法在处理多种声学语义风格变化时的局限性。通过利用语音语言模型的最新进展，研究者开发了一种简单的无监督分割方法，并在多个实验设置中得到验证，结果显示该方法在边界检测、分段纯度和过度分割方面优于现有基线。此研究对改进语音处理技术具有重要意义。

无监督语音分割：基于语音语言模型的通用方法