洞察 - Speech Synthesis - # Zero-shot text-to-speech

Improving Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts

Q: How can the proposed multi-scale acoustic prompt approach be extended to other speech-related tasks beyond zero-shot TTS, such as voice conversion or speech enhancement?

The multi-scale acoustic prompt approach proposed in the context of zero-shot TTS can be extended to other speech-related tasks by adapting the model architecture and training strategies to suit the specific requirements of tasks like voice conversion or speech enhancement. For voice conversion, the model can be trained to learn not only the speaker's style but also the specific characteristics of the target speaker's voice. By incorporating additional conditioning information related to the desired voice characteristics, the model can be fine-tuned to generate speech that mimics the target speaker's voice while preserving the linguistic content. Similarly, for speech enhancement tasks, the model can be trained to focus on capturing and enhancing specific acoustic features in the input speech signal, such as reducing noise or improving clarity. By adjusting the input prompts and the training objectives, the model can be tailored to address the unique challenges posed by voice conversion or speech enhancement tasks.

Q: What are the potential limitations of the current approach, and how could it be further improved to handle more diverse speaker characteristics or speaking styles?

One potential limitation of the current approach is the reliance on a fixed number of sentences in the style prompt to capture the speaker's characteristics. This limitation may restrict the model's ability to handle speakers with highly diverse speaking styles or those who exhibit variations in their speech patterns across different contexts. To address this limitation, the model could be enhanced by incorporating adaptive mechanisms that dynamically adjust the level of detail captured in the style prompt based on the complexity of the speaker's characteristics. Additionally, introducing hierarchical or multi-level representations of speaker features could enable the model to capture a broader range of speaker characteristics and adapt more effectively to diverse speaking styles. By incorporating techniques such as hierarchical attention mechanisms or multi-scale feature extraction, the model could improve its ability to handle speakers with varying characteristics and speaking styles.

Q: Given the advancements in language models and neural audio codecs, what other novel architectures or training strategies could be explored to push the boundaries of zero-shot speech synthesis?

To further advance zero-shot speech synthesis, novel architectures and training strategies could be explored to leverage the latest advancements in language models and neural audio codecs. One potential direction is the integration of meta-learning techniques to enable the model to quickly adapt to new speakers with minimal adaptation data. By incorporating meta-learning frameworks that facilitate rapid adaptation to unseen speakers, the model could enhance its zero-shot capabilities and improve the quality of synthesized speech for a wider range of speakers. Additionally, exploring self-supervised learning approaches that leverage unlabeled data to learn speaker-specific features could enhance the model's ability to generalize to new speakers and capture subtle variations in speaking styles. By combining self-supervised learning with meta-learning and hierarchical feature representations, novel architectures could be developed to push the boundaries of zero-shot speech synthesis and achieve even higher levels of naturalness and speaker similarity in synthesized speech.

核心概念

A novel zero-shot text-to-speech model that utilizes multi-scale acoustic prompts, including a style prompt to capture personal speaking style and a timbre prompt to preserve the target speaker's voice characteristics, outperforming state-of-the-art language model-based approaches in terms of naturalness and speaker similarity.

摘要

The paper proposes a novel zero-shot text-to-speech (TTS) model that utilizes multi-scale acoustic prompts to capture both the timbre and personal speaking style of the target speaker.

The key components are:

Speaker-aware text encoder: Extracts personal speaking style information from a style prompt (multiple sentences) and fuses it into the text embeddings using a reference attention module.
Acoustic decoder: Based on the VALL-E model, it generates speech with the same timbre as a 3-second timbre prompt, while incorporating the speaker-aware text embeddings.

The experimental results show that the proposed model outperforms state-of-the-art language model-based zero-shot TTS approaches in terms of naturalness and speaker similarity. The performance is further improved by increasing the number of sentences in the style prompt.

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A 3-second speech segment can be used as the timbre prompt to clone the target speaker's voice characteristics.
Multiple sentences (e.g., 10) from the target speaker can be used as the style prompt to capture the personal speaking style.

引用

"To further improve speaker similarity for language model-based zero-shot TTS synthesis, we propose to utilize multi-scale acoustic prompts to capture both the timbre and personal speaking style of the target speaker."
"Our proposed method outperforms state-of-the-art language model-based zero-shot TTS model [16] and other baselines in terms of naturalness and speaker similarity."
"The performance is also improved with an increasing number of sentences used in the style prompt during inference."

从中提取的关键见解

Improving Language Model-Based Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts

by Shun Lei,Yix... 在 arxiv.org 04-10-2024

https://arxiv.org/pdf/2309.11977.pdf

Improving Language Model-Based Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts

更深入的查询

How can the proposed multi-scale acoustic prompt approach be extended to other speech-related tasks beyond zero-shot TTS, such as voice conversion or speech enhancement?

The multi-scale acoustic prompt approach proposed in the context of zero-shot TTS can be extended to other speech-related tasks by adapting the model architecture and training strategies to suit the specific requirements of tasks like voice conversion or speech enhancement. For voice conversion, the model can be trained to learn not only the speaker's style but also the specific characteristics of the target speaker's voice. By incorporating additional conditioning information related to the desired voice characteristics, the model can be fine-tuned to generate speech that mimics the target speaker's voice while preserving the linguistic content. Similarly, for speech enhancement tasks, the model can be trained to focus on capturing and enhancing specific acoustic features in the input speech signal, such as reducing noise or improving clarity. By adjusting the input prompts and the training objectives, the model can be tailored to address the unique challenges posed by voice conversion or speech enhancement tasks.

What are the potential limitations of the current approach, and how could it be further improved to handle more diverse speaker characteristics or speaking styles?

One potential limitation of the current approach is the reliance on a fixed number of sentences in the style prompt to capture the speaker's characteristics. This limitation may restrict the model's ability to handle speakers with highly diverse speaking styles or those who exhibit variations in their speech patterns across different contexts. To address this limitation, the model could be enhanced by incorporating adaptive mechanisms that dynamically adjust the level of detail captured in the style prompt based on the complexity of the speaker's characteristics. Additionally, introducing hierarchical or multi-level representations of speaker features could enable the model to capture a broader range of speaker characteristics and adapt more effectively to diverse speaking styles. By incorporating techniques such as hierarchical attention mechanisms or multi-scale feature extraction, the model could improve its ability to handle speakers with varying characteristics and speaking styles.

Given the advancements in language models and neural audio codecs, what other novel architectures or training strategies could be explored to push the boundaries of zero-shot speech synthesis?

To further advance zero-shot speech synthesis, novel architectures and training strategies could be explored to leverage the latest advancements in language models and neural audio codecs. One potential direction is the integration of meta-learning techniques to enable the model to quickly adapt to new speakers with minimal adaptation data. By incorporating meta-learning frameworks that facilitate rapid adaptation to unseen speakers, the model could enhance its zero-shot capabilities and improve the quality of synthesized speech for a wider range of speakers. Additionally, exploring self-supervised learning approaches that leverage unlabeled data to learn speaker-specific features could enhance the model's ability to generalize to new speakers and capture subtle variations in speaking styles. By combining self-supervised learning with meta-learning and hierarchical feature representations, novel architectures could be developed to push the boundaries of zero-shot speech synthesis and achieve even higher levels of naturalness and speaker similarity in synthesized speech.