Hierarchical Acoustic Modeling for Token-Based Zero-Shot Text-to-Speech

Core Concepts

The author introduces the HAM-TTS model, emphasizing its hierarchical acoustic modeling approach and data augmentation strategy to enhance zero-shot text-to-speech synthesis.

Abstract

The HAM-TTS model addresses challenges in token-based TTS models by incorporating a novel hierarchical acoustic modeling approach. It focuses on improving pronunciation accuracy, speaking style consistency, and timbre continuity through innovative strategies and extensive training data.
The model integrates a latent variable sequence with refined self-supervised learning units to mitigate pronunciation errors and style mutations. Data augmentation techniques are strategically employed to enhance timbre uniformity in synthesized speech.
Additionally, a few-shot voice conversion model is utilized to generate diverse voices with consistent content but varied timbres, enriching speech diversity. Comparative experiments demonstrate the model's superiority over existing methods in terms of pronunciation precision and maintaining speaking style.
The paper provides a comprehensive overview of the research methodology, including related works, experimental setup, results analysis, and future research directions.

Stats

The data size was scaled up to 650k hours.
The zero-shot TTS model had 0.8B parameters.
The CER for HAM-TTS-S was 4.0%.

Quotes

"Our method incorporates a latent variable sequence containing supplementary acoustic information based on refined self-supervised learning discrete units into the TTS model."
"Comparative experiments demonstrate our model’s superiority over VALL-E in pronunciation precision and maintaining speaking style."
"The substantial requirement for large and diverse training data further limits their widespread adoption."

Key Insights Distilled From

HAM-TTS

by Chunhui Wang... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05989.pdf

Deeper Inquiries

How can synthetic data be optimized for speaker diversity in speech synthesis models?

Synthetic data can be optimized for speaker diversity in speech synthesis models by carefully selecting a diverse set of speakers to generate the synthetic data. This selection should include speakers with various accents, genders, ages, and vocal characteristics to ensure a broad representation of voices. Additionally, varying the content and context of the speech prompts used to generate synthetic data can help capture different speaking styles and linguistic nuances.
To further enhance speaker diversity, techniques such as style transfer or voice conversion can be applied to manipulate the synthesized voices into new variations while maintaining naturalness. By incorporating these strategies, speech synthesis models trained on synthetic data will have exposure to a wide range of vocal characteristics and styles, leading to more versatile and inclusive output.

How are K-Means clustering implications on refining HuBERT features for speech synthesis?

K-Means clustering plays a crucial role in refining HuBERT features for speech synthesis by removing personalized information such as speaking styles from the original HuBERT features. This refinement process enables the TTS model to focus solely on core acoustic information essential for improving pronunciation accuracy and maintaining consistent speaking style throughout synthesized speech.
By applying K-Means clustering to HuBERT features, irrelevant variations that could lead to inconsistencies in speaking style are eliminated. The refined features provide more accurate acoustic information that enhances the overall quality of synthesized speech. This ensures that the TTS model focuses on relevant acoustic details while generating natural-sounding and coherent output.

How can the inference speed of HAM-TTS model be improved for real-time applications?

The inference speed of HAM-TTS model can be improved for real-time applications through several optimization strategies:

Model Architecture Optimization: Streamlining the architecture of HAM-TTS by reducing unnecessary layers or parameters can significantly improve inference speed without compromising performance.

Quantization Techniques: Implementing quantization techniques like weight quantization or dynamic quantization reduces computational complexity during inference, leading to faster processing times.

Hardware Acceleration: Utilizing specialized hardware accelerators like GPUs or TPUs tailored for neural network computations speeds up inference tasks.

Parallel Processing: Implementing parallel processing techniques allows simultaneous computation across multiple cores or devices, optimizing resource utilization and enhancing speed.

Caching Mechanisms: Employing caching mechanisms for frequently accessed computations helps reduce redundant calculations during inference sessions.

By implementing these optimization strategies tailored towards efficient inference processing, HAM-TTS model's real-time applicability is enhanced with faster response times suitable for interactive user interactions or time-sensitive applications.