Existing mean opinion score (MOS) prediction models typically predict utterance-level naturalness MOS and can be insensitive to localized pitch-accent errors. We propose Pitch-Accent-focused Speech Quality Assessment (PASQA), which explicitly targets pitch-accent correctness. To train our model, we construct a controlled Japanese accent-error dataset by changing accent patterns using an accent-controllable text-to-speech system, and compute a pseudo accent-quality score from the accent-error rate. PASQA builds on self-supervised representations and employs mora-conditioned fusion, ranking loss, an auxiliary accent-error localization task, and speaker-invariant training. Experiments show that conventional models fail to preserve the ordering by accent-error severity, whereas PASQA achieves high ordering accuracy on both seen and unseen speakers. Further, PASQA shows stronger agreement with human accent-correctness judgments. The code is available at https://github.com/lycorp-jp/PASQA.
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. We investigate this via controlled perturbations on speech: acoustic degradation, prosodic errors, and manipulation of speaker-specific characteristics such as pitch and speaking rate. We obtained MOS predictions for these speech samples from both human listeners and the model, and analyzed the differences in their perceptual characteristics. Results show that most models track acoustic degradation well, while all are insensitive to prosodic errors despite large subjective score drops. For speaker characteristics, models exhibit a double dissociation: strong mean fundamental frequency (F0) biases absent in human ratings, yet insensitivity to speaking rate and F0 variability that humans notice. These findings highlight limitations of scalar MOS prediction beyond acoustic fidelity.
Mean Opinion Score (MOS) is the gold standard for evaluating synthesized speech naturalness. However, current automatic MOS predictors are dominated by self-supervised learning (SSL) models that prioritize high-level semantics, potentially compromising their ability to capture critical acoustic details. In this paper, we systematically investigate representations from three paradigms: SSLs, acoustic-only neural audio codecs (NACs), and unified NACs that integrate semantics into reconstruction-based architectures. Extensive benchmarking on the standard BVCC and multiple out-of-domain (OOD) datasets demonstrates that features synergizing semantic understanding with fine-grained acoustic modeling achieve a higher performance upper bound in speech quality assessment. Ultimately, our findings highlight that semantics alone are not enough; a dual focus on semantic content and acoustic fidelity is essential for robust MOS prediction.
Accent text-to-speech (TTS) aims to synthesize speech with a target accent while preserving speaker identity, but faces two key challenges: disentangling accent from speaker characteristics and effectively conditioning speech generation on the two disentangled factors. In this paper, we propose Joycent, a diffusion-based accent TTS framework that addresses both challenges. Our key idea is to separate accent and speaker information in both representation learning and TTS conditioning. Joycent uses WhisAID, a Whisper-based accent encoder with gradient reversal to learn speaker-disentangled accent representations, and introduces layer-specific conditional layer normalization to inject accent and speaker information at different stages of the text encoder. We evaluate Joycent on the Mandarin Regional Accent Corpus (MRAC) with seen and unseen speakers, including a challenging cross-accent setting where the speaker and accent prompts come from different accents. Experimental results show that Joycent improves accent similarity over existing methods while maintaining strong speaker similarity, with consistent gains under the challenging cross-accent setting. Subjective evaluation further confirms improved naturalness, accent similarity, and speaker preservation. The audio samples are available at https://oshindow.github.io/joycent/.