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arXiv · 2603.21078

Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbation

Tianle Yang·Chengzhe Sun·Phil Rose·Cassandra L. Jacobs·Siwei Lyu
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Abstract

This study proposes a segmental-level prosodic probing framework to evaluate neural TTS models' ability to reproduce consonant-induced f0 perturbation, a fine-grained segmental-prosodic effect that reflects local articulatory mechanisms. We compare synthetic and natural speech realizations for thousands of words, stratified by lexical frequency, using Tacotron 2 and FastSpeech 2 trained on the same speech corpus (LJ Speech). These controlled analyses are then complemented by a large-scale evaluation spanning multiple advanced TTS systems. Results show accurate reproduction for high-frequency words but poor generalization to low-frequency items, suggesting that the examined TTS architectures rely more on lexical-level memorization than on abstract segmental-prosodic encoding. This finding highlights a limitation in such TTS systems' ability to generalize prosodic detail beyond seen data. The proposed probe offers a linguistically informed diagnostic framework that may inform future TTS evaluation methods, and has implications for interpretability and authenticity assessment in synthetic speech.

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Tianle Yang, Chengzhe Sun, Phil Rose, Cassandra L. Jacobs, Siwei Lyu. 2026-03-22. Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbation. https://doi.org/10.1016/j.csl.2026.101983

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