indsigt - Speech Synthesis - # Pause modeling for Korean text-to-speech

Enhancing Korean Text-to-Speech Synthesis through Integrated Modeling of Syntactic and Acoustic Cues for Improved Pause Formation

Q: How can the proposed framework be extended to other languages with different linguistic characteristics to improve pause modeling in text-to-speech synthesis?

The proposed framework's approach of incorporating both syntactic and acoustic cues for optimizing pause formation in Korean TTS can be extended to other languages by adapting the model to the specific linguistic characteristics of each language. For languages with different syntactic structures, the model can be modified to account for unique grammar rules, word order, and sentence structures. This adaptation may involve adjusting the syntactic parsing module to capture the language-specific syntactic features that influence pause placement. Furthermore, for languages with distinct acoustic properties, the model can be tailored to consider additional acoustic cues that are relevant for natural speech production. This may include incorporating features such as intonation patterns, pitch variations, rhythm, and stress patterns specific to the target language. By training the model on data from the target language and fine-tuning the parameters to reflect its linguistic nuances, the framework can be optimized for improved pause modeling in text-to-speech synthesis across a variety of languages.

Q: What other acoustic features, beyond those explored in this work, could be leveraged to further enhance the naturalness of synthetic speech?

In addition to the acoustic features explored in the proposed framework, several other acoustic cues could be leveraged to further enhance the naturalness of synthetic speech. Some of these features include: Pitch Contour: Incorporating pitch contour information can help in capturing the melodic variations in speech, contributing to the prosodic naturalness of synthesized utterances. Speech Rate and Tempo: Modeling variations in speech rate and tempo can make synthetic speech sound more human-like, as individuals naturally adjust their speaking speed based on context and emphasis. Emotional Prosody: Including acoustic features related to emotional prosody, such as changes in pitch, intensity, and duration influenced by emotions, can add a layer of expressiveness to synthetic speech. Articulatory Dynamics: Modeling articulatory dynamics, including features related to the movement of articulators during speech production, can improve the realism and clarity of synthesized speech. Background Noise Adaptation: Incorporating adaptive mechanisms to account for background noise levels can help in producing clearer and more intelligible synthetic speech in varying acoustic environments. By integrating these additional acoustic features into the text-to-speech synthesis framework, the naturalness and expressiveness of synthetic speech can be further enhanced, making it more engaging and lifelike for listeners.

Q: Given the ethical considerations around speech synthesis technologies, how can the responsible development and deployment of systems like TaKOtron2-Pro be ensured to mitigate potential misuse or deception?

Responsible development and deployment of speech synthesis technologies like TaKOtron2-Pro require adherence to ethical guidelines and best practices to mitigate potential misuse or deception. Some key strategies to ensure responsible use of such systems include: Transparency and Disclosure: Developers should be transparent about the capabilities and limitations of the technology, clearly disclosing when synthetic speech is being used. Ethical Review and Oversight: Conducting ethical reviews of the technology's potential impact on society and obtaining oversight from regulatory bodies can help ensure responsible development and deployment. User Consent and Control: Providing users with clear information about the use of synthetic speech and giving them control over its generation and dissemination can empower individuals to make informed choices. Bias and Fairness Considerations: Mitigating bias in speech synthesis models and ensuring fairness in the representation of diverse voices and accents can prevent discriminatory outcomes. Security and Privacy Safeguards: Implementing robust security measures to protect against unauthorized access and safeguarding user privacy by handling sensitive data responsibly are essential aspects of responsible deployment. Education and Awareness: Promoting public awareness about the capabilities and limitations of speech synthesis technologies can help prevent misuse and foster responsible usage. By incorporating these measures into the development and deployment of systems like TaKOtron2-Pro, developers can uphold ethical standards, promote trust among users, and mitigate the risks associated with potential misuse or deception of synthetic speech technologies.

Kernekoncepter

Leveraging the interplay between syntactic and acoustic cues to enhance pause prediction and placement for more natural Korean text-to-speech synthesis, even for longer and more complex sentences.

Resumé

The paper proposes a novel framework, TaKOtron2-Pro, that integrates both syntactic and acoustic information to improve pause modeling for Korean text-to-speech (TTS) synthesis.

Key highlights:

Syntactic features are extracted using a combination of local context modeling (CBHL) and global constituent parsing (NCP), capturing both local and global linguistic relationships.
Acoustic features are learned in an unsupervised manner using a target acoustic embedding (TAE) predicted by the text encoder.
The integration of syntactic and acoustic cues enables TaKOtron2-Pro to accurately and robustly insert pauses, even for longer and more complex sentences that are out-of-domain compared to the training data.
Evaluations show that TaKOtron2-Pro significantly outperforms baseline TTS models in terms of mean opinion score (MOS), ABX preference, and word error rate (WER), especially for longer utterances.
Ablation studies confirm the importance of leveraging both syntactic and acoustic information for effective pause modeling in Korean TTS.

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Statistik

The Korean KSS dataset used for training has an average audio length of 2.38 seconds.
The average length of the longer, out-of-domain sentences used for evaluation is 22 words per sentence.

Citater

"Towards the enhancement of verbal fluency in synthetic Korean speech, we concentrate on refining the modeling of respiratory pauses."
"Remarkably, our framework possesses the capability to consistently generate natural speech even for considerably more extended and intricate out-of-domain (OOD) sentences, despite its training on short audio clips."
"Architectural design choices are validated through comparisons with baseline models and ablation studies using subjective and objective metrics, thus confirming model performance."

Vigtigste indsigter udtrukket fra

Leveraging the Interplay Between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation

by Yejin Jeon,Y... kl. arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02592.pdf

Leveraging the Interplay Between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation

Dybere Forespørgsler

How can the proposed framework be extended to other languages with different linguistic characteristics to improve pause modeling in text-to-speech synthesis?

The proposed framework's approach of incorporating both syntactic and acoustic cues for optimizing pause formation in Korean TTS can be extended to other languages by adapting the model to the specific linguistic characteristics of each language. For languages with different syntactic structures, the model can be modified to account for unique grammar rules, word order, and sentence structures. This adaptation may involve adjusting the syntactic parsing module to capture the language-specific syntactic features that influence pause placement.
Furthermore, for languages with distinct acoustic properties, the model can be tailored to consider additional acoustic cues that are relevant for natural speech production. This may include incorporating features such as intonation patterns, pitch variations, rhythm, and stress patterns specific to the target language. By training the model on data from the target language and fine-tuning the parameters to reflect its linguistic nuances, the framework can be optimized for improved pause modeling in text-to-speech synthesis across a variety of languages.

What other acoustic features, beyond those explored in this work, could be leveraged to further enhance the naturalness of synthetic speech?

In addition to the acoustic features explored in the proposed framework, several other acoustic cues could be leveraged to further enhance the naturalness of synthetic speech. Some of these features include:

Pitch Contour: Incorporating pitch contour information can help in capturing the melodic variations in speech, contributing to the prosodic naturalness of synthesized utterances.

Speech Rate and Tempo: Modeling variations in speech rate and tempo can make synthetic speech sound more human-like, as individuals naturally adjust their speaking speed based on context and emphasis.

Emotional Prosody: Including acoustic features related to emotional prosody, such as changes in pitch, intensity, and duration influenced by emotions, can add a layer of expressiveness to synthetic speech.

Articulatory Dynamics: Modeling articulatory dynamics, including features related to the movement of articulators during speech production, can improve the realism and clarity of synthesized speech.

Background Noise Adaptation: Incorporating adaptive mechanisms to account for background noise levels can help in producing clearer and more intelligible synthetic speech in varying acoustic environments.

By integrating these additional acoustic features into the text-to-speech synthesis framework, the naturalness and expressiveness of synthetic speech can be further enhanced, making it more engaging and lifelike for listeners.

Given the ethical considerations around speech synthesis technologies, how can the responsible development and deployment of systems like TaKOtron2-Pro be ensured to mitigate potential misuse or deception?

Responsible development and deployment of speech synthesis technologies like TaKOtron2-Pro require adherence to ethical guidelines and best practices to mitigate potential misuse or deception. Some key strategies to ensure responsible use of such systems include:

Transparency and Disclosure: Developers should be transparent about the capabilities and limitations of the technology, clearly disclosing when synthetic speech is being used.

Ethical Review and Oversight: Conducting ethical reviews of the technology's potential impact on society and obtaining oversight from regulatory bodies can help ensure responsible development and deployment.

User Consent and Control: Providing users with clear information about the use of synthetic speech and giving them control over its generation and dissemination can empower individuals to make informed choices.

Bias and Fairness Considerations: Mitigating bias in speech synthesis models and ensuring fairness in the representation of diverse voices and accents can prevent discriminatory outcomes.

Security and Privacy Safeguards: Implementing robust security measures to protect against unauthorized access and safeguarding user privacy by handling sensitive data responsibly are essential aspects of responsible deployment.

Education and Awareness: Promoting public awareness about the capabilities and limitations of speech synthesis technologies can help prevent misuse and foster responsible usage.

By incorporating these measures into the development and deployment of systems like TaKOtron2-Pro, developers can uphold ethical standards, promote trust among users, and mitigate the risks associated with potential misuse or deception of synthetic speech technologies.