Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911535095349248 |
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| author | Yang, Tianle Sun, Chengzhe Rose, Phil Jacobs, Cassandra L. Lyu, Siwei |
| author_facet | Yang, Tianle Sun, Chengzhe Rose, Phil Jacobs, Cassandra L. Lyu, Siwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21078 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbation Yang, Tianle Sun, Chengzhe Rose, Phil Jacobs, Cassandra L. Lyu, Siwei Computation and Language Artificial Intelligence Sound 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. |
| title | Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbation |
| topic | Computation and Language Artificial Intelligence Sound |
| url | https://arxiv.org/abs/2603.21078 |