Learning-free L2-Accented Speech Generation using Phonological Rules
arXiv:2603.07550v1 Announce Type: new Abstract: Accent plays a crucial role in speaker identity and inclusivity in speech technologies. Existing accented text-to-speech (TTS) systems either require large-scale accented datasets or lack fine-grained phoneme-level controllability. We propose a accented TTS framework that combines phonological rules with a multilingual TTS model. The rules are applied to phoneme sequences to transform accent at the phoneme level while preserving intelligibility. The method requires no accented training data and enables explicit phoneme-level accent manipulation. We design rule sets for Spanish- and Indian-accented English, modeling systematic differences in consonants, vowels, and syllable structure arising from phonotactic constraints. We analyze the trade-off between phoneme-level duration alignment and accent as realized in speech timing. Experimental results demonstrate effective accent shift while maintaining speech quality.
arXiv:2603.07550v1 Announce Type: new Abstract: Accent plays a crucial role in speaker identity and inclusivity in speech technologies. Existing accented text-to-speech (TTS) systems either require large-scale accented datasets or lack fine-grained phoneme-level controllability. We propose a accented TTS framework that combines phonological rules with a multilingual TTS model. The rules are applied to phoneme sequences to transform accent at the phoneme level while preserving intelligibility. The method requires no accented training data and enables explicit phoneme-level accent manipulation. We design rule sets for Spanish- and Indian-accented English, modeling systematic differences in consonants, vowels, and syllable structure arising from phonotactic constraints. We analyze the trade-off between phoneme-level duration alignment and accent as realized in speech timing. Experimental results demonstrate effective accent shift while maintaining speech quality.
Executive Summary
This article proposes a novel approach to generating L2-accented speech using phonological rules and a multilingual TTS model. The framework, which requires no accented training data, enables explicit phoneme-level accent manipulation while preserving intelligibility. The authors design rule sets for Spanish- and Indian-accented English, modeling systematic differences in consonants, vowels, and syllable structure. Experimental results demonstrate effective accent shift while maintaining speech quality. This approach has the potential to improve inclusivity in speech technologies by allowing for more accurate and nuanced representation of accented speech.
Key Points
- ▸ Phonological rules are used to transform accent at the phoneme level
- ▸ The framework requires no accented training data
- ▸ Explicit phoneme-level accent manipulation is enabled
Merits
Strength in Linguistic Representation
The framework's ability to model systematic differences in consonants, vowels, and syllable structure between languages is a significant strength.
Advancements in Speech Synthesis
The proposed approach has the potential to improve speech synthesis technologies by enabling more accurate and nuanced representation of accented speech.
Demerits
Limited Generalizability
The framework may not be generalizable to other languages or accents without significant modifications.
Potential for Overemphasis on Accent
The focus on accent manipulation may lead to an overemphasis on this aspect, potentially neglecting other important aspects of speech synthesis.
Expert Commentary
While the proposed framework shows promise, its limitations and potential biases should be carefully considered. The focus on phonological rules may lead to an oversimplification of the complex relationships between accent, language, and culture. Furthermore, the framework's reliance on rule-based systems may not be sufficient for handling the nuances of real-world speech. Nevertheless, the article's contributions to the field of speech synthesis and accent representation are significant, and its novel approach has the potential to spark further research and innovation.
Recommendations
- ✓ Further research should be conducted to evaluate the framework's generalizability to other languages and accents.
- ✓ The development of more sophisticated rule-based systems or machine learning approaches should be explored to improve the framework's ability to handle complex speech phenomena.