arXiv:2509. 24457v1 Announce Type: cross Abstract: Objective speech-quality metrics are widely used to assess codec performance.
arXiv:2608.31037v1 Announce Type: new Abstract: Neural audio codecs (NACs) convert speech into discrete token sequences, and prior work has reported that these sequences follow language-like statisti...
arXiv:2609.26306v1 Announce Type: new Abstract: This work presents the task and results of the CHiME-9 challenge for Enhancing Conversations to address Hearing Impairment. The challenge considers the...
arXiv:2606. 19951v1 Announce Type: cross Abstract: Mean opinion score (MOS) prediction models are widely used as proxy metrics in text-to-speech (TTS) research, yet their ability to capture quality differences beyond acoustic fidelity remains unclear.
arXiv:2608. 02235v1 Announce Type: cross Abstract: Recent advances in neural text-to-speech (TTS) systems have substantially improved speech naturalness and intelligibility across many languages.
arXiv:2608. 05727v1 Announce Type: cross Abstract: Neural Audio Codecs are widely adopted in speech generation and editing.
arXiv:2608.21176v1 Announce Type: cross Abstract: Automatic speech quality assessment aims to predict Mean Opinion Scores (MOS) consistent with human subjective perception and is essential for evalua...
arXiv:2606. 19597v1 Announce Type: cross Abstract: Mean opinion scores (MOS) are widely used for speech quality assessment, yet scalar labels are sensitive to rater variability and listening test differences.
Mean opinion scores (MOS) are widely used for speech quality assessment, yet scalar labels are sensitive to rater variability and listening test differences. This introduces labeling noise, which limits the reliability of MOS prediction.
arXiv:2607. 14846v1 Announce Type: cross Abstract: Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes spoken language from its textual representation.
arXiv:2608. 09930v1 Announce Type: cross Abstract: Automated Text-to-Speech (TTS) evaluation methods (Mean Opinion Score (MOS) predictors and Audio Large Language Models (Audio-LLM) judges) are expected to reflect human perception, yet it is unclear how well they capture the distinct aspects of speech that listeners actually perceive.
arXiv:2606. 20137v1 Announce Type: cross Abstract: Existing mean opinion score (MOS) prediction models typically predict utterance-level naturalness MOS and can be insensitive to localized pitch-accent errors.
The paper introduces Masked Autoregressive Speech Enhancement (MARSE), a method that iteratively decodes masked clean speech frames using continuous latent representations from a neural audio codec (DAC). Unlike prior approaches that relied on discrete token representations, MARSE employs a Conformer model and explores various decoding policies to balance speech enhancement performance with computational cost. The authors provide audio examples and code online to demonstrate the method’s effectiveness.