Cross-Utterance Conditioned Variational Autoencoder for Enhancing Prosody and Naturalness in Speech Synthesis

المفاهيم الأساسية

The proposed Cross-Utterance Conditioned Variational Autoencoder (CUC-VAE) framework leverages contextual information from surrounding utterances to generate more natural and expressive speech by modeling prosody.

الملخص

The paper presents the Cross-Utterance Conditioned Variational Autoencoder Speech Synthesis (CUC-VAE S2) framework to enhance the expressiveness and naturalness of synthesized speech. The key components are:

1. Cross-Utterance (CU) Embedding: This module extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features.

2. Cross-Utterance Enhanced CVAE: This module estimates the posterior of latent prosody features for each phoneme, enhancing the VAE encoder with an utterance-specific prior to generate more natural prosody.

The authors propose two specialized algorithms based on the CUC-VAE S2 framework:

1. CUC-VAE TTS for text-to-speech, which generates audio with contextual prosody using surrounding text data.

2. CUC-VAE SE for speech editing, which samples real mel spectrograms using contextual data to facilitate flexible text edits while maintaining high fidelity and naturalness.

Experiments on the LibriTTS dataset show that the proposed CUC-VAE TTS and CUC-VAE SE systems significantly outperform baseline methods in terms of prosody diversity, naturalness, and intelligibility of the synthesized speech.

الإحصائيات

The CUC-VAE TTS model achieves superior performance in prosody diversity compared to baseline systems.
The CUC-VAE SE system demonstrates a significant ability to sustain high fidelity and enhance naturalness across a spectrum of editing operations compared to the baseline.

اقتباسات

"The CUC-VAE TTS algorithm is a direct implementation of our framework, aiming to produce audio that carries contextual prosody using surrounding text data."
"The CUC-VAE SE samples real mel spectrograms using contextual data, creating authentic-sounding audio that supports versatile text edits."

الرؤى الأساسية المستخلصة من

Cross-Utterance Conditioned VAE for Speech Generation

by Yang Li, Che... في arxiv.org 09-20-2024

https://arxiv.org/pdf/2309.04156.pdf

استفسارات أعمق

How can the CUC-VAE framework be extended to other speech-related tasks beyond text-to-speech and speech editing, such as voice conversion or emotional speech synthesis?

The Cross-Utterance Conditioned Variational Autoencoder (CUC-VAE) framework can be effectively extended to various speech-related tasks, including voice conversion and emotional speech synthesis, by leveraging its core architecture and principles.

Voice Conversion: The CUC-VAE framework can be adapted for voice conversion by conditioning the model on speaker-specific features. By incorporating speaker embeddings from both the source and target speakers, the CUC-VAE can learn to map the prosodic and acoustic features of the source voice to those of the target voice. This can be achieved by modifying the CU-Embedding module to include speaker identity information, allowing the model to generate speech that retains the content of the source while adopting the characteristics of the target speaker.

Emotional Speech Synthesis: For emotional speech synthesis, the CUC-VAE framework can be enhanced by integrating emotional labels or embeddings into the conditioning process. By training the model on a dataset annotated with emotional states, the CUC-VAE can learn to generate speech that reflects specific emotions. This can be accomplished by augmenting the CU-Embedding module to include emotional context, allowing the model to produce prosodic variations that correspond to different emotional expressions.

Multimodal Integration: The CUC-VAE framework can also be extended to incorporate multimodal inputs, such as visual cues or textual sentiment analysis, to further enhance the expressiveness of the generated speech. By integrating visual features from facial expressions or gestures, the model can produce speech that is not only contextually relevant but also emotionally resonant.

By adapting the CUC-VAE framework in these ways, it can address a broader range of speech synthesis applications, enhancing its versatility and effectiveness in generating natural and expressive speech.

What are the potential limitations of the CUC-VAE approach, and how could it be further improved to handle more complex prosody patterns or multilingual scenarios?

While the CUC-VAE framework presents significant advancements in speech synthesis, it also faces several limitations that could hinder its performance in more complex scenarios.

Complex Prosody Patterns: One limitation is the model's ability to capture intricate prosody patterns, especially in cases of overlapping speech or rapid dialogue exchanges. The current architecture may struggle to accurately model these nuances due to its reliance on contextual embeddings from surrounding utterances. To improve this, the framework could incorporate hierarchical attention mechanisms that allow for a more granular analysis of prosodic features at different levels (e.g., phoneme, word, and sentence levels). Additionally, integrating recurrent neural networks (RNNs) or long short-term memory (LSTM) networks could help in capturing temporal dependencies more effectively.

Multilingual Scenarios: The CUC-VAE framework may also face challenges in multilingual speech synthesis, as different languages exhibit distinct prosodic and phonetic characteristics. To address this, the model could be trained on multilingual datasets that include diverse linguistic features. Furthermore, incorporating language-specific embeddings or using a language identification module could help the model adapt its prosody generation based on the language being synthesized.

Data Scarcity: The performance of the CUC-VAE framework is heavily dependent on the quality and quantity of training data. In scenarios where high-quality multilingual or emotional datasets are scarce, the model may not generalize well. Techniques such as transfer learning, where the model is pre-trained on a large dataset and fine-tuned on a smaller, task-specific dataset, could enhance its adaptability and performance in low-resource settings.

By addressing these limitations through architectural enhancements and data strategies, the CUC-VAE framework can be better equipped to handle complex prosody patterns and multilingual scenarios, ultimately improving the naturalness and expressiveness of synthesized speech.

Given the importance of contextual information in speech synthesis, how could the CUC-VAE framework be integrated with other emerging techniques in natural language processing, such as large language models or few-shot learning, to enhance its capabilities?

The integration of the CUC-VAE framework with emerging techniques in natural language processing (NLP), such as large language models (LLMs) and few-shot learning, can significantly enhance its capabilities in speech synthesis.

Integration with Large Language Models: By incorporating LLMs like GPT-3 or BERT, the CUC-VAE framework can leverage their advanced contextual understanding and language generation capabilities. The LLM can provide rich contextual embeddings that capture semantic nuances and relationships between words, which can be fed into the CU-Embedding module. This would allow the CUC-VAE to generate more contextually relevant prosody and intonation patterns, improving the naturalness and expressiveness of the synthesized speech. Additionally, LLMs can assist in generating diverse and coherent text inputs, which can be particularly beneficial for tasks like storytelling or dialogue generation.

Few-Shot Learning: The application of few-shot learning techniques can enable the CUC-VAE framework to adapt to new tasks or languages with minimal training data. By employing meta-learning strategies, the model can learn to generalize from a few examples, allowing it to synthesize speech in new contexts or styles without extensive retraining. This is particularly useful in scenarios where labeled data is scarce, such as for low-resource languages or specific emotional tones. The CUC-VAE can be fine-tuned on a small set of examples, quickly adapting its prosody generation capabilities to match the desired output.

Contextual Adaptation: Combining the CUC-VAE framework with attention mechanisms from LLMs can enhance its ability to adapt to varying contexts dynamically. By utilizing attention layers that focus on relevant parts of the input text or surrounding utterances, the model can better capture the nuances of speech that depend on context, such as sarcasm or emphasis. This would lead to more nuanced and contextually appropriate speech synthesis.

By integrating the CUC-VAE framework with these advanced NLP techniques, it can achieve a higher level of contextual awareness and adaptability, resulting in more natural, expressive, and contextually relevant speech synthesis across a variety of applications.