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Unified Front-End Framework for English Text-to-Speech Synthesis

Core Concepts
Proposing a unified front-end framework for English text-to-speech synthesis to enhance performance across linguistic modules.
Abstract: English TTS front-end components: TN, PWPP, G2P. Proposal of a unified front-end framework. Achieving state-of-the-art performance in all modules. Introduction: Importance of the front-end in English TTS synthesis. Components: TN, PWPP, G2P. Focus on improving individual modules. Methodology: Framework Overview: Shared multi-task model for TN, PWPP, G2P. Modules: TN Module: Hybrid method combining rules and models. PWPP Module: Hierarchical sequence tagging structure. G2P Module: Utilizing lexicon and tasks like POS and Polyphone. Experimental Evaluations: Experimental Settings: Evaluation metrics: SER, F1-score, WER. Experimental Results and Analysis: TN Module: Achieving the best SER compared to other methods. PWPP Module: Hierarchical tagging method outperforming traditional method. G2POOV Task & G2P Module: Proposed method achieving the best WER in G2POOV task and complete G2P module. Conclusion: Proposal of a unified front-end framework for English TTS synthesis with superior performance across all modules.
The proposed method achieves an SER of 1.19% in the TN module. The hierarchical sequence tagging method shows F1-scores of 90.83%, 57.65%, and 83.36% for different prosody levels in the PWPP module. The proposed method achieves a WER of 19.42% in the G2POOV task and 3.09% in the complete G2P module.
"Our approach exhibits greater flexibility within the TN module." "Our proposed method is based on a BERT model fine-tuned for multiple front-end tasks." "Our evaluation metric is the word error rate (WER), where a word is deemed incorrect if the predicted output does not match the reference exactly."

Key Insights Distilled From

by Zelin Ying,C... at 03-26-2024
A unified front-end framework for English text-to-speech synthesis

Deeper Inquiries

How can this unified front-end framework be adapted to other languages?

The unified front-end framework proposed in the context for English text-to-speech synthesis can be adapted to other languages by following a similar approach but tailoring it to the specific linguistic characteristics of each language. The key steps would involve: Language-specific Data Collection: Gather a large dataset of text and corresponding speech samples in the target language. Text Normalization Module: Develop rules and models that cater to the unique normalization requirements of the new language, such as handling special characters, accents, or grammar structures. Prosody Word Prosody Phrase Module: Identify pause boundaries and prosodic features that are characteristic of the target language's speech patterns. Grapheme-to-Phoneme Module: Create mappings between graphemes (written symbols) and phonemes (speech sounds) specific to the new language, considering its phonetic intricacies. By customizing these modules according to the linguistic nuances of different languages, this unified framework can effectively adapt to support text-to-speech synthesis in various languages.

What are potential drawbacks or limitations of relying heavily on models for text normalization?

Relying heavily on models for text normalization may have several drawbacks or limitations: Limited Generalizability: Models trained on specific datasets may struggle with generalizing well to unseen data or variations outside their training domain. Overfitting: Complex models might overfit noisy training data, leading to inaccuracies when applied to real-world scenarios. Lack of Interpretability: Highly complex models could lack interpretability, making it challenging for developers or users to understand how decisions are made during normalization processes. Computational Resources: Training and deploying model-heavy solutions require significant computational resources which might not be feasible in all environments. Balancing model reliance with rule-based approaches can help mitigate these limitations by providing more robustness and transparency in text normalization tasks.

How might advancements in speech synthesis impact industries beyond voice assistants and audiobooks?

Advancements in speech synthesis have far-reaching implications across various industries beyond voice assistants and audiobooks: Accessibility Services: Improved speech synthesis technology can enhance accessibility services for individuals with visual impairments through screen readers and audio descriptions. Customer Service: Industries like banking, retail, and healthcare can leverage advanced speech synthesis for interactive voice response systems improving customer service experiences. Language Learning: Speech synthesis developments aid language learners by providing accurate pronunciation guides and conversational practice opportunities tailored per learner's needs. Entertainment & Gaming: Enhanced speech synthesis capabilities enable more realistic character dialogues in video games, virtual reality experiences, animated films enhancing user immersion. Overall, advancements in speech synthesis technologies have transformative potential across diverse sectors by enabling more natural human-computer interactions while opening up innovative applications yet unexplored before now within these industries.