insight - Natural Language Processing - # Expressive Text-to-Speech Synthesis with Emphasis Prediction

Emphatic Expressive Text-to-Speech with Linguistic Information for Improved Expressiveness and Naturalness

Q: How can the unsupervised emphasis labeling process be further improved to eliminate the need for human-labeled data during fine-tuning

To further improve the unsupervised emphasis labeling process and potentially eliminate the need for human-labeled data during fine-tuning, several strategies can be considered: Enhanced Signal Processing Techniques: Utilize advanced signal processing techniques to extract more nuanced features related to emphasis, such as pitch variations, energy levels, and duration patterns. By refining the algorithms used for unsupervised labeling, the system can better identify emphasis without human intervention. Integration of Machine Learning Models: Incorporate machine learning models, such as deep learning algorithms, to analyze the audio signals and automatically detect emphasis patterns. By training these models on a large corpus of data, they can learn to recognize emphasis cues effectively. Semi-Supervised Learning: Implement a semi-supervised learning approach where a small amount of human-labeled data is used to guide the model initially. As the model gains more experience and refines its understanding of emphasis, it can gradually reduce its reliance on human-labeled data. Active Learning Strategies: Implement active learning techniques where the model actively selects the most informative data points for human annotation. By focusing on the most challenging or ambiguous cases, the model can learn more efficiently and reduce the overall need for human-labeled data. By combining these approaches and continuously refining the unsupervised emphasis labeling process, it is possible to enhance the accuracy and reliability of emphasis detection, ultimately reducing the dependency on human-labeled data during fine-tuning.

Q: What other linguistic features beyond syntax and semantics could be explored to enhance the expressiveness and naturalness of the generated speech

Beyond syntax and semantics, exploring additional linguistic features can further enhance the expressiveness and naturalness of generated speech. Some potential linguistic features to consider include: Prosody Patterns: Incorporating prosodic features such as intonation, rhythm, and stress patterns can significantly impact the expressiveness of speech. By modeling these prosodic elements, the TTS system can generate speech with more natural and emotive qualities. Pragmatics and Discourse Structure: Considering pragmatic aspects like conversational implicature, speech acts, and discourse structure can help the TTS system generate speech that is contextually appropriate and coherent. Understanding the underlying discourse can improve the flow and coherence of the generated speech. Morphological and Phonological Features: Analyzing morphological and phonological characteristics of the text can contribute to more accurate pronunciation and phrasing in the synthesized speech. By incorporating these features, the TTS system can produce speech that aligns closely with the linguistic norms of the target language. Emotion and Sentiment Analysis: Integrating emotion and sentiment analysis techniques can enable the TTS system to infuse appropriate emotional cues into the generated speech. By recognizing and reflecting emotional content in the text, the system can produce speech that conveys the intended emotional tone effectively. By exploring and incorporating a diverse range of linguistic features beyond syntax and semantics, the TTS system can achieve a more comprehensive understanding of the text and generate speech that is not only expressive and natural but also contextually rich and engaging.

Q: How can the proposed EE-TTS framework be extended to other languages and domains beyond Mandarin text-to-speech

To extend the proposed EE-TTS framework to other languages and domains beyond Mandarin text-to-speech, several adaptation strategies can be implemented: Language-specific Linguistic Models: Develop language-specific linguistic models that capture the unique phonological, prosodic, and syntactic characteristics of the target language. By training the model on diverse language datasets, it can adapt to the linguistic nuances of different languages effectively. Multilingual Training: Implement multilingual training techniques to enhance the model's ability to generalize across languages. By exposing the model to multiple languages during training, it can learn universal linguistic principles and adapt more easily to new languages. Domain-specific Fine-tuning: Fine-tune the EE-TTS model on domain-specific datasets to tailor the speech generation to specific domains such as medical, legal, or technical fields. By incorporating domain-specific vocabulary and speech patterns, the model can produce speech that is more relevant and accurate in specialized domains. Cross-lingual Transfer Learning: Explore cross-lingual transfer learning approaches to transfer knowledge from one language to another. By leveraging pre-trained models in one language and adapting them to new languages, the model can expedite the learning process and improve performance in diverse linguistic contexts. By implementing these adaptation strategies and considering the linguistic diversity and specific requirements of different languages and domains, the EE-TTS framework can be successfully extended to a wide range of languages and applications beyond Mandarin text-to-speech.

Conceitos Básicos

EE-TTS, a novel text-to-speech model, leverages multi-level linguistic information from syntax and semantics to generate highly expressive speech with appropriate emphasis, outperforming baseline systems in both expressiveness and naturalness.

Resumo

The paper proposes Emphatic Expressive TTS (EE-TTS), a novel text-to-speech (TTS) model that utilizes linguistic information from syntax and semantics to generate emphatic expressive speech without emphasis labels.

Key highlights:

EE-TTS consists of three main components: a linguistic information extractor, an emphasis predictor, and a conditioned acoustic model.
The linguistic information extractor extracts syntactic information (Part-of-Speech tags and Dependency Parsing) and semantic information (from pre-trained BERT) from the input text.
The emphasis predictor uses the extracted linguistic information to predict the positions of emphasis in the text.
The conditioned acoustic model generates expressive speech conditioned on the predicted emphasis positions and linguistic embedding.
EE-TTS is pre-trained on a large dataset with unsupervised emphasis labels generated using a signal-based method, and then fine-tuned on datasets with human-labeled emphasis.
Experimental results show that EE-TTS outperforms baseline systems in both expressiveness and naturalness, with MOS improvements of 0.49 and 0.67 respectively.
Ablation studies demonstrate the effectiveness of each component of the linguistic information and the chosen architecture.
EE-TTS also exhibits strong generalization across different datasets, as shown by the AB preference test results.

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Estatísticas

EE-TTS outperforms the baseline by 0.49 in expressiveness MOS and 0.67 in naturalness MOS.
The F0-RMSE metric gradually decreases as each linguistic module is added, consistent with the MOS score increase.
The emphasis predictor achieves a reasonable precision of 0.87, indicating the predicted emphasis positions are appropriate for human perception.

Citações

"By fully exploiting linguistic information (syntax and semantics), EE-TTS can predict more reasonable emphasis positions from the text."
"Conditioned on the appropriate emphasis position and linguistic information, EE-TTS can consistently synthesize more expressive and natural speech with emphasis position."
"High robustness and great generalization ability of EE-TTS are demonstrated according to experimental results."

Principais Insights Extraídos De

EE-TTS: Emphatic Expressive TTS with Linguistic Information

by Yi Zhong,Che... às arxiv.org 04-16-2024

https://arxiv.org/pdf/2305.12107.pdf

EE-TTS: Emphatic Expressive TTS with Linguistic Information

Perguntas Mais Profundas

How can the unsupervised emphasis labeling process be further improved to eliminate the need for human-labeled data during fine-tuning

To further improve the unsupervised emphasis labeling process and potentially eliminate the need for human-labeled data during fine-tuning, several strategies can be considered:

Enhanced Signal Processing Techniques: Utilize advanced signal processing techniques to extract more nuanced features related to emphasis, such as pitch variations, energy levels, and duration patterns. By refining the algorithms used for unsupervised labeling, the system can better identify emphasis without human intervention.

Integration of Machine Learning Models: Incorporate machine learning models, such as deep learning algorithms, to analyze the audio signals and automatically detect emphasis patterns. By training these models on a large corpus of data, they can learn to recognize emphasis cues effectively.

Semi-Supervised Learning: Implement a semi-supervised learning approach where a small amount of human-labeled data is used to guide the model initially. As the model gains more experience and refines its understanding of emphasis, it can gradually reduce its reliance on human-labeled data.

Active Learning Strategies: Implement active learning techniques where the model actively selects the most informative data points for human annotation. By focusing on the most challenging or ambiguous cases, the model can learn more efficiently and reduce the overall need for human-labeled data.

By combining these approaches and continuously refining the unsupervised emphasis labeling process, it is possible to enhance the accuracy and reliability of emphasis detection, ultimately reducing the dependency on human-labeled data during fine-tuning.

What other linguistic features beyond syntax and semantics could be explored to enhance the expressiveness and naturalness of the generated speech

Beyond syntax and semantics, exploring additional linguistic features can further enhance the expressiveness and naturalness of generated speech. Some potential linguistic features to consider include:

Prosody Patterns: Incorporating prosodic features such as intonation, rhythm, and stress patterns can significantly impact the expressiveness of speech. By modeling these prosodic elements, the TTS system can generate speech with more natural and emotive qualities.

Pragmatics and Discourse Structure: Considering pragmatic aspects like conversational implicature, speech acts, and discourse structure can help the TTS system generate speech that is contextually appropriate and coherent. Understanding the underlying discourse can improve the flow and coherence of the generated speech.

Morphological and Phonological Features: Analyzing morphological and phonological characteristics of the text can contribute to more accurate pronunciation and phrasing in the synthesized speech. By incorporating these features, the TTS system can produce speech that aligns closely with the linguistic norms of the target language.

Emotion and Sentiment Analysis: Integrating emotion and sentiment analysis techniques can enable the TTS system to infuse appropriate emotional cues into the generated speech. By recognizing and reflecting emotional content in the text, the system can produce speech that conveys the intended emotional tone effectively.

By exploring and incorporating a diverse range of linguistic features beyond syntax and semantics, the TTS system can achieve a more comprehensive understanding of the text and generate speech that is not only expressive and natural but also contextually rich and engaging.

How can the proposed EE-TTS framework be extended to other languages and domains beyond Mandarin text-to-speech

To extend the proposed EE-TTS framework to other languages and domains beyond Mandarin text-to-speech, several adaptation strategies can be implemented:

Language-specific Linguistic Models: Develop language-specific linguistic models that capture the unique phonological, prosodic, and syntactic characteristics of the target language. By training the model on diverse language datasets, it can adapt to the linguistic nuances of different languages effectively.

Multilingual Training: Implement multilingual training techniques to enhance the model's ability to generalize across languages. By exposing the model to multiple languages during training, it can learn universal linguistic principles and adapt more easily to new languages.

Domain-specific Fine-tuning: Fine-tune the EE-TTS model on domain-specific datasets to tailor the speech generation to specific domains such as medical, legal, or technical fields. By incorporating domain-specific vocabulary and speech patterns, the model can produce speech that is more relevant and accurate in specialized domains.

Cross-lingual Transfer Learning: Explore cross-lingual transfer learning approaches to transfer knowledge from one language to another. By leveraging pre-trained models in one language and adapting them to new languages, the model can expedite the learning process and improve performance in diverse linguistic contexts.

By implementing these adaptation strategies and considering the linguistic diversity and specific requirements of different languages and domains, the EE-TTS framework can be successfully extended to a wide range of languages and applications beyond Mandarin text-to-speech.