A Simple HMM with Self-Supervised Representations for Phone Segmentation
<|reference_start|>A Simple HMM with Self-Supervised Representations for Phone Segmentation: 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.<|reference_end|>
@article{yang2024a,
    title={A Simple HMM with Self-Supervised Representations for Phone Segmentation},
    author={Gene-Ping Yang and Hao Tang},
    journal={arXiv preprint arXiv:2409.09646},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09646},
    primaryClass={cs.CL}
}