Idée - Machine Learning - # Multi-speaker Expressive Speech Synthesis

Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning

Q: How can the proposed approach be extended to handle more diverse speaking styles and emotions, including those not seen during training?

The proposed approach can be extended to handle more diverse speaking styles and emotions by incorporating techniques such as data augmentation, transfer learning, and continual learning. Data Augmentation: By augmenting the training data with variations in speaking styles and emotions, the model can learn to generalize better to unseen styles and emotions. Techniques like speed perturbation, pitch shifting, and background noise addition can help create a more diverse dataset. Transfer Learning: Leveraging pre-trained models on a larger and more diverse dataset can help the model capture a broader range of styles and emotions. Fine-tuning the pre-trained model on the target dataset can enable it to adapt to new styles and emotions more effectively. Continual Learning: Implementing a continual learning strategy allows the model to adapt to new styles and emotions over time. By periodically updating the model with new data and retraining on a combination of old and new data, the model can continuously improve its ability to synthesize diverse speech. Adversarial Training: Introducing adversarial training techniques can help the model learn robust representations that are invariant to variations in speaking styles and emotions. Adversarial training can encourage the model to disentangle style, emotion, and speaker representations, leading to better generalization.

Q: What are the potential limitations of the contrastive learning-based approach in terms of scalability and robustness to noisy or sparse data?

While contrastive learning has shown promising results in various domains, including speech processing, there are some limitations to consider: Scalability: Contrastive learning methods often require large amounts of data to learn meaningful representations effectively. Scaling contrastive learning to massive datasets can be computationally expensive and may require specialized hardware for efficient training. Robustness to Noisy Data: Contrastive learning is sensitive to noisy or mislabeled data, which can negatively impact the quality of learned representations. Noisy data can introduce inconsistencies in the learned embeddings, leading to suboptimal performance. Sparse Data: In scenarios where data is sparse or imbalanced across different styles and emotions, contrastive learning may struggle to capture the underlying patterns effectively. Sparse data can result in biased representations and hinder the model's ability to generalize to unseen data. Hyperparameter Sensitivity: Contrastive learning models often rely on tuning various hyperparameters, such as the margin parameter or temperature parameter. Finding the optimal hyperparameters can be challenging and may require extensive experimentation.

Q: Could the learned style, emotion, and speaker representations be leveraged for other applications beyond speech synthesis, such as speech recognition or voice conversion?

The learned style, emotion, and speaker representations can indeed be valuable for various other applications in the field of speech processing: Speech Recognition: The disentangled representations can enhance speech recognition systems by providing additional context about the speaking style and emotional content of the speech. This information can help improve the accuracy of speech recognition models, especially in scenarios where the speaker's emotional state may affect speech patterns. Voice Conversion: Leveraging the learned representations can facilitate voice conversion tasks by enabling the transformation of speech from one style or emotion to another while preserving the speaker's identity. The disentangled representations can guide the conversion process to ensure that the converted speech retains the desired style and emotion. Speaker Verification: The disentangled speaker representations can be utilized for speaker verification tasks, where the goal is to authenticate the identity of a speaker based on their voice. By incorporating the learned speaker embeddings, speaker verification systems can achieve better performance and robustness against spoofing attacks. Emotion Recognition: The extracted emotion representations can be applied to emotion recognition systems to improve the detection and classification of emotions in speech. By leveraging the disentangled emotion embeddings, emotion recognition models can better capture the subtle variations in emotional expression and enhance their overall performance.

Concepts de base

A novel contrastive learning-based approach to extract disentangled style, emotion, and speaker representations from speech, enabling multi-speaker expressive speech synthesis.

Résumé

The paper proposes a novel contrastive learning-based approach for multi-speaker expressive speech synthesis. The key aspects are:

Speech Representation Learning (SRL) Module:

Employs contrastive learning to extract disentangled style, emotion, and speaker representations from speech.
Constructs positive and negative sample pairs at both utterance and category levels to leverage labeled and unlabeled data.
Minimizes mutual information between the extracted representations to achieve better disentanglement.

Expressive VITS Model:

Integrates the learned style, emotion, and speaker representations into an improved VITS model for expressive speech synthesis.
Replaces the stochastic duration predictor and MAS module in VITS with the duration predictor and length regulator from FastSpeech2.
Adds a flow-based style adaptor to improve the prosody of synthetic speech.

The experiments on a multi-domain dataset demonstrate that the proposed approach can synthesize diverse stylistic and emotional speech for a target speaker, outperforming previous methods in terms of naturalness, emotion similarity, speaker similarity, and style similarity.

Stats

The proposed approach achieves the lowest character error rate (CER) of 3.9% and word error rate (WER) of 2.7% compared to the baseline models.
The proposed approach obtains the highest speaker cosine similarity of 0.896, indicating better speaker disentanglement.

Citations

"Contrastive learning is a method to learn the desired features of the data via constructing positive and negative samples."
"We introduce a semi-supervised training strategy to the proposed approach, which can effectively leverage multi-domain data, including style-labeled, emotion-labeled, and unlabeled data."
"Experimental results show that our proposed framework can synthesize diverse stylistic and emotional speech for a target speaker who does not have the target style or emotion in the training data."

Idées clés tirées de

Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning

by Xinfa Zhu,Yu... à arxiv.org 04-26-2024

https://arxiv.org/pdf/2310.17101.pdf

Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning

Questions plus approfondies

How can the proposed approach be extended to handle more diverse speaking styles and emotions, including those not seen during training?

The proposed approach can be extended to handle more diverse speaking styles and emotions by incorporating techniques such as data augmentation, transfer learning, and continual learning.

Data Augmentation: By augmenting the training data with variations in speaking styles and emotions, the model can learn to generalize better to unseen styles and emotions. Techniques like speed perturbation, pitch shifting, and background noise addition can help create a more diverse dataset.

Transfer Learning: Leveraging pre-trained models on a larger and more diverse dataset can help the model capture a broader range of styles and emotions. Fine-tuning the pre-trained model on the target dataset can enable it to adapt to new styles and emotions more effectively.

Continual Learning: Implementing a continual learning strategy allows the model to adapt to new styles and emotions over time. By periodically updating the model with new data and retraining on a combination of old and new data, the model can continuously improve its ability to synthesize diverse speech.

Adversarial Training: Introducing adversarial training techniques can help the model learn robust representations that are invariant to variations in speaking styles and emotions. Adversarial training can encourage the model to disentangle style, emotion, and speaker representations, leading to better generalization.

What are the potential limitations of the contrastive learning-based approach in terms of scalability and robustness to noisy or sparse data?

While contrastive learning has shown promising results in various domains, including speech processing, there are some limitations to consider:

Scalability: Contrastive learning methods often require large amounts of data to learn meaningful representations effectively. Scaling contrastive learning to massive datasets can be computationally expensive and may require specialized hardware for efficient training.

Robustness to Noisy Data: Contrastive learning is sensitive to noisy or mislabeled data, which can negatively impact the quality of learned representations. Noisy data can introduce inconsistencies in the learned embeddings, leading to suboptimal performance.

Sparse Data: In scenarios where data is sparse or imbalanced across different styles and emotions, contrastive learning may struggle to capture the underlying patterns effectively. Sparse data can result in biased representations and hinder the model's ability to generalize to unseen data.

Hyperparameter Sensitivity: Contrastive learning models often rely on tuning various hyperparameters, such as the margin parameter or temperature parameter. Finding the optimal hyperparameters can be challenging and may require extensive experimentation.

Could the learned style, emotion, and speaker representations be leveraged for other applications beyond speech synthesis, such as speech recognition or voice conversion?

The learned style, emotion, and speaker representations can indeed be valuable for various other applications in the field of speech processing:

Speech Recognition: The disentangled representations can enhance speech recognition systems by providing additional context about the speaking style and emotional content of the speech. This information can help improve the accuracy of speech recognition models, especially in scenarios where the speaker's emotional state may affect speech patterns.

Voice Conversion: Leveraging the learned representations can facilitate voice conversion tasks by enabling the transformation of speech from one style or emotion to another while preserving the speaker's identity. The disentangled representations can guide the conversion process to ensure that the converted speech retains the desired style and emotion.

Speaker Verification: The disentangled speaker representations can be utilized for speaker verification tasks, where the goal is to authenticate the identity of a speaker based on their voice. By incorporating the learned speaker embeddings, speaker verification systems can achieve better performance and robustness against spoofing attacks.

Emotion Recognition: The extracted emotion representations can be applied to emotion recognition systems to improve the detection and classification of emotions in speech. By leveraging the disentangled emotion embeddings, emotion recognition models can better capture the subtle variations in emotional expression and enhance their overall performance.

Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning