Jul 2026· International Conference on Signal Processing and Communications· pp. 1-5· 0 citations· 28 references
Abstract
Text-to-speech systems are improving fast, but measuring how natural they sound still requires expensive human listening tests. Existing automatic methods struggle to generalise well across different datasets. We present QMOS, a MOS prediction framework that extracts hierarchical speech quality features from a frozen Qwen2-Audio large audio language model and combines them with layer-weighted WavLM SSL representations through a learned cross-attention fusion. This design captures both high-level semantic naturalness and lowlevel acoustic distortions in a unified model. On two standard benchmarks, SOMOS and BVCC, QMOS achieves competitive system-level SRCC of 0.923 and 0.921 on SOMOS and BVCC, respectively, while using no system-ID conditioning or listener embeddings. Cross-domain evaluation yields a competitive SRCC of 0.702, suggesting the learned representations generalise across acoustic domains.
Results confirm that while models perform significantly better on seen samples, frozen SSL embeddings combined with deep Transformer encoders offer the most stable and scalable solution for universal speech quality assessment.
Experimental results show that VALL-E outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity and could preserve the speaker’s emotion and acoustic environment from the prompt in synthesis.
CASA, a simpler architecture combining Whisper-medium and Qwen3.5-2B that achieves state-of-the-art performance while providing a more interpretable separation between speech delivery and content, is proposed.
Nhan Phan, Ilona Lähteenmäki, Anna von Zansen et al.· 0 citations
Deep learning speech enhancement models are trained without grounding in acoustic physics, and evaluations remain confined almost exclusively to English. We address both gaps with MRAN-UNet, which embeds Harmonic Frequency Attention (HFA) - a parameter-free module derived from the source-filter model that aggregates sp...
Satya Prasad Gaddamedi, Debolina Pramanik, Puja Bharati et al.· International Conference on...· 0 citations
Overall, matched-text speech delivery should be treated as a first-class factor in Audio LLM safety evaluation by holding transcript content fixed and varying six speech-delivery presets whose acoustic attributes may co-vary.
A reproducible, multi-metric benchmarking framework for systematic evaluation of modern TTS systems through domain-specific analysis, which reveals substantial variation in TTS performance across speech domains, with emotional speech consistently presenting the greatest synthesis challenge.
Ali B. Jafar, Amal Sarmad, Shifa Yousaf et al.· 0 citations
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