Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbation
arXiv:2603.21078v1 Announce Type: new 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
arXiv:2603.21078v1 Announce Type: new 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.
Executive Summary
This study evaluates the ability of neural text-to-speech (TTS) systems to model consonant-induced F0 perturbation, a crucial segmental-prosodic effect in speech production. Researchers propose a segmental-level prosodic probing framework to assess the performance of Tacotron 2 and FastSpeech 2 models, trained on the LJ Speech corpus. Results indicate that these TTS systems accurately reproduce high-frequency words but struggle with low-frequency items, suggesting reliance on lexical-level memorization rather than abstract prosodic encoding. This limitation affects the generalizability of these systems beyond seen data. The study contributes a linguistically informed diagnostic framework for future TTS evaluation and has implications for synthetic speech interpretability and authenticity assessment.
Key Points
- ▸ The study proposes a novel segmental-level prosodic probing framework to evaluate TTS systems.
- ▸ The framework assesses the performance of Tacotron 2 and FastSpeech 2 models on consonant-induced F0 perturbation.
- ▸ Results indicate the importance of lexical frequency in TTS system performance, with high-frequency words being accurately reproduced but low-frequency items being poorly generalized.
Merits
Strength of the proposed framework
The segmental-level prosodic probing framework provides a linguistically informed diagnostic tool for evaluating TTS systems, enabling researchers to assess the prosodic detail of synthetic speech.
Insight into TTS system limitations
The study highlights a key limitation of neural TTS systems, specifically their reliance on lexical-level memorization rather than abstract prosodic encoding, affecting their generalizability beyond seen data.
Demerits
Limited generalizability
The study's findings are based on a specific TTS system (Tacotron 2 and FastSpeech 2) and speech corpus (LJ Speech), limiting the generalizability of the results to other systems and corpora.
Narrow focus on consonant-induced F0 perturbation
The study's focus on a specific segmental-prosodic effect may not capture the full range of prosodic phenomena that TTS systems should be able to model.
Expert Commentary
This study is a significant contribution to the field of TTS research, highlighting a crucial limitation of current systems and providing a novel framework for evaluating their performance. The findings have far-reaching implications for synthetic speech interpretability, authenticity assessment, and TTS system evaluation methods. However, the study's narrow focus on consonant-induced F0 perturbation and limited generalizability to other systems and corpora should be addressed in future research. Nevertheless, this work serves as a crucial stepping stone for the development of more advanced TTS systems that can capture the complex prosodic nuances of human speech.
Recommendations
- ✓ Future studies should investigate the performance of TTS systems on a wider range of prosodic phenomena to better understand their limitations and potential.
Sources
Original: arXiv - cs.CL
