arXiv:2606. 11542v1 Announce Type: cross Abstract: Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized representations.
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arXiv:2606. 17835v1 Announce Type: cross Abstract: This study examines the extent to which the wav2vec2.
arXiv:2609.10434v1 Announce Type: new Abstract: Self-supervised speech foundation models are now used in a wide array of downstream applications, including traditional speech recognition and as the b...
arXiv:2609.28060v1 Announce Type: new Abstract: Self-supervised speech models encode rich phonetic information, but it remains unclear how to transform this information into interpretable metrics for...
arXiv:2606. 16019v1 Announce Type: cross Abstract: Expert phonetic annotation is costly, especially for non-standard dialects and atypical speech.
arXiv:2607. 09020v1 Announce Type: cross Abstract: Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately.
arXiv:2609.13045v1 Announce Type: new Abstract: Speech-to-speech translation (S2ST) has advanced significantly with speech LLMs, offering the potential for joint optimization and preserving non-lingu...