用梅尔频谱峰检测做基线，结合自监督表示提升语音分割性能。
A Simple HMM with Self-Supervised Representations for Phone Segmentation
- 基于梅尔频谱峰检测构建简单隐马尔可夫模型
- 在多个数据集上优于主流自监督方法，提升明显
- 适合语音识别、声学建模等语音处理任务研究者
尽管自监督表示近期取得进展，无监督音素分割仍具挑战性。多数方法致力于通过自监督学习改进音素表示，期望其能迁移至音素分割任务。本文相反地表明，对梅尔频谱图进行峰值检测是一种强基线，优于许多自监督方法。基于此发现，我们提出一种简单的隐马尔可夫模型，利用自监督表示及边界特征进行音素分割。实验结果表明，该方法在多个基准上持续优于先前方法，且具备通用形式，支持灵活设计调整。
原文摘要 · Abstract (English)
Despite the recent advance in self-supervised representations, unsupervised phonetic segmentation remains challenging. Most approaches focus on improving phonetic representations with self-supervised learning, with the hope that the improvement can transfer to phonetic segmentation. In this paper, contrary to recent approaches, we show that peak detection on Mel spectrograms is a strong baseline, better than many self-supervised approaches. Based on this finding, we propose a simple hidden Markov model that uses self-supervised representations and features at the boundaries for phone segmentation. Our results demonstrate consistent improvements over previous approaches, with a generalized formulation allowing versatile design adaptations.
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