Expressive Voice Conversion with Soft Speech Units and Adversarial Style Augmentation
核心概念
A novel framework for expressive voice conversion based on soft speech units, adversarial style augmentation, and knowledge distillation for prosody modeling.
摘要
The proposed "SAVC" framework utilizes soft speech units from HuBert-Soft as input and consists of the following key components:
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Adversarial Style Augmentation (ASA) module: This module applies dynamic statistic perturbation to the input soft speech units to eliminate speaker-related information. The attribute encoder is encouraged to learn similar speaker-independent features from the perturbed samples.
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Attribute Encoder: This encoder extracts content and prosody features independently from the perturbed soft speech units. The content embedding and prosody embedding are then used by the decoder to reconstruct the target speech.
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Prosody Modeling: To disentangle prosody from content, a teacher model with a pre-trained prosody encoder is employed. Knowledge distillation is used to guide the student model to learn expressive prosody embedding without requiring explicit prosodic features as input.
The experiments show that the proposed SAVC framework outperforms previous voice conversion methods in terms of naturalness, timbre similarity, and prosody similarity, even for unseen speakers in the zero-shot setting. The ablation studies further validate the effectiveness of the key components, including the adversarial style augmentation and prosody modeling.
Learning Expressive Disentangled Speech Representations with Soft Speech Units and Adversarial Style Augmentation
統計資料
The intelligibility and naturalness of converted speech from SAVC outperform previous work in both many-to-many and zero-shot voice conversion tasks.
SAVC achieves lower Mel-Cepstral Distortion (MCD) and higher Speaker Embedding Similarity (SES) compared to baseline models.
The Pearson correlation coefficients of energy and pitch for SAVC are higher than other models with explicit prosody modeling, indicating better preservation of expressive prosody.
引述
"We propose a novel framework named 'SAVC' which is based on Soft speech units and Adversarial style augmentation for Voice Conversion."
"To address these issues, we propose an adversarial style augmentation to impose dynamic statistic perturbation on features to reduce timbre leakage on hidden units."
"We apply knowledge distillation in prosody modeling. The student model can learn expressive prosody embedding without requiring the explicit prosodic features."
深入探究
How can the proposed SAVC framework be extended to handle emotional or paralinguistic information in voice conversion
The proposed SAVC framework can be extended to handle emotional or paralinguistic information in voice conversion by incorporating additional features related to emotions and paralinguistic cues. One approach could be to integrate emotion recognition models that can analyze the emotional content of the input speech and extract relevant emotional features. These features can then be used in conjunction with the existing content and prosody features to enhance the expressiveness of the converted speech.
Furthermore, the framework can be augmented with paralinguistic information such as tone, emphasis, and speaking rate. By incorporating models that can extract paralinguistic cues from the input speech, the SAVC system can better capture the nuances of speech delivery and mimic these aspects in the converted speech. This integration of emotional and paralinguistic information can result in more natural and expressive voice conversions that convey not only the content but also the emotional and stylistic elements of the original speech.
What are the potential limitations of the adversarial style augmentation approach, and how could it be further improved
One potential limitation of the adversarial style augmentation approach is the risk of overfitting to the specific style perturbations introduced during training. If the model becomes too reliant on the specific perturbations used in training, it may struggle to generalize to unseen styles or variations in speech patterns. To address this limitation, the approach could be further improved by introducing a more diverse set of style perturbations during training. By exposing the model to a wider range of style variations, it can learn to disentangle speaker-related information more effectively and produce more robust voice conversions.
Another limitation could be the computational complexity of the adversarial style augmentation module, especially when dealing with large-scale datasets or complex speech features. Optimizing the efficiency of the perturbation process and streamlining the training pipeline could help mitigate this limitation and make the approach more scalable for real-world applications.
Given the importance of prosody modeling, how could the integration of prosodic features from other modalities, such as text or video, enhance the expressiveness of the converted speech
Integrating prosodic features from other modalities, such as text or video, can significantly enhance the expressiveness of the converted speech by providing additional context and cues for prosody modeling. For example, text-based prosodic features like punctuation, sentence structure, and emphasis markers can be used to guide the prosody modeling process and ensure that the converted speech aligns with the intended emotional and expressive content of the text.
Similarly, video-based prosodic features such as facial expressions, gestures, and body language can offer valuable insights into the emotional and expressive aspects of speech delivery. By incorporating these multimodal prosodic cues into the SAVC framework, the model can generate more nuanced and contextually relevant prosody in the converted speech. This integration of prosodic features from diverse modalities can lead to more natural and emotionally expressive voice conversions that capture the full range of human communication cues.
