Neural networks for Text-to-Speech evaluation

Ilya Trofimenko; David Kocharyan; Aleksandr Zaitsev; Pavel Repnikov; Mark Levin; Nikita Shevtsov

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arXiv · 2604.08562

Neural networks for Text-to-Speech evaluation

Ilya Trofimenko·David Kocharyan·Aleksandr Zaitsev·Pavel Repnikov·Mark Levin·Nikita Shevtsov

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Abstract

Ensuring that Text-to-Speech (TTS) systems deliver human-perceived quality at scale is a central challenge for modern speech technologies. Human subjective evaluation protocols such as Mean Opinion Score (MOS) and Side-by-Side (SBS) comparisons remain the de facto gold standards, yet they are expensive, slow, and sensitive to pervasive assessor biases. This study addresses these barriers by formulating, and implementing a suite of novel neural models designed to approximate expert judgments in both relative (SBS) and absolute (MOS) settings. For relative assessment, we propose NeuralSBS, a HuBERT-backed model achieving 73.7% accuracy (on SOMOS dataset). For absolute assessment, we introduce enhancements to MOSNet using custom sequence-length batching, as well as WhisperBert, a multimodal stacking ensemble that combines Whisper audio features and BERT textual embeddings via weak learners. Our best MOS models achieve a Root Mean Square Error (RMSE) of ~0.40, significantly outperforming the human inter-rater RMSE baseline of 0.62. Furthermore, our ablation studies reveal that naively fusing text via cross-attention can degrade performance, highlighting the effectiveness of ensemble-based stacking over direct latent fusion. We additionally report negative results with SpeechLM-based architectures and zero-shot LLM evaluators (Qwen2-Audio, Gemini 2.5 flash preview), reinforcing the necessity of dedicated metric learning frameworks.

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BibTeX RIS

Ilya Trofimenko, David Kocharyan, Aleksandr Zaitsev, Pavel Repnikov, Mark Levin, Nikita Shevtsov. 2026-03-17. Neural networks for Text-to-Speech evaluation. https://arxiv.org/abs/2604.08562

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