CLARITY: Contextual Linguistic Adaptation and Accent Retrieval for Dual-Bias Mitigation in Text-to-Speech Generation
Abstract
Instruction-guided text-to-speech (TTS) research has reached a maturity level where excellent speech generation quality is possible on demand, yet two coupled biases persist: accent bias, where models default to dominant phonetic patterns, and linguistic bias, where dialect-specific lexical and cultural cues are ignored. These biases are interdependent, as authentic accent generation requires both accent fidelity and localized text. We present Contextual Linguistic Adaptation and Retrieval for Inclusive TTS sYnthesis (CLARITY), a backbone-agnostic framework that addresses these biases through dual-signal optimization: (i) contextual linguistic adaptation that localizes input text to the target dialect, and (ii) retrieval-augmented accent prompting (RAAP) that supplies accent-consistent speech prompts. Across twelve English accents, CLARITY improves accent accuracy and fairness while maintaining strong perceptual quality.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of CLARITY, a novel framework for mitigating accent and linguistic biases in text-to-speech generation, leveraging dual-signal optimization through contextual linguistic adaptation and accent retrieval. This work significantly advances the field of TTS by addressing critical biases that affect speech authenticity and user experience, showcasing a rigorous methodology and comprehensive evaluation.
Methodology
The methodology presented in CLARITY is innovative, leveraging large language models (LLMs) for contextual linguistic adaptation and accent retrieval. The dual-signal optimization approach effectively addresses accent and linguistic biases in TTS systems, which are often overlooked in existing literature. The framework's backbone-agnostic nature allows it to be integrated with various TTS models, enhancing its applicability. The use of LLMs to parse user instructions and adapt text for specific dialects demonstrates a strong understanding of sociolinguistic factors, making the approach both practical and theoretically sound.
Results & evaluation
The experiments are robust, involving a diverse set of twelve English accents and employing both objective and subjective evaluation metrics. The use of human listening tests alongside automated metrics like NISQA and accent accuracy provides a comprehensive assessment of the system's performance. The ablation studies further validate the effectiveness of the proposed methods, showcasing improvements in accent fidelity and fairness. However, the results could benefit from a larger participant pool in subjective evaluations to enhance statistical significance.
Reproducibility
The paper provides a clear description of the experimental setup, datasets, and evaluation metrics, which aids reproducibility. The availability of code and audio samples on GitHub is a significant step towards ensuring that other researchers can replicate the findings. However, more detailed instructions on the setup and execution of experiments would enhance reproducibility further.
Limitations
One limitation is the potential for bias in the LLMs used for text adaptation and evaluation, which may affect the results. Additionally, while the framework shows promise for English accents, its applicability to other languages and dialects remains untested. The reliance on specific datasets could also limit generalizability, and the paper does not address how the model would perform with less represented accents or languages.
Broader impact
The implications of this research are significant, particularly in promoting inclusivity in TTS systems. By addressing accent and linguistic biases, CLARITY has the potential to enhance user experience in applications such as virtual assistants, audiobooks, and language learning tools. The framework could also inform future research on bias mitigation in AI systems, contributing to more equitable technology. The main contribution of this paper is the introduction of CLARITY, a novel framework for mitigating accent and linguistic biases in text-to-speech generation, leveraging dual-signal optimization through contextual linguistic adaptation and accent retrieval. This work significantly advances the field of TTS by addressing critical biases that affect speech authenticity and user experience, showcasing a rigorous methodology and comprehensive evaluation.
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