Sample Efficient Adaptive Text-to-Speech (1809.10460v3)

Abstract: We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three strategies: (i) learning the speaker embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire architecture with stochastic gradient descent, and (iii) predicting the speaker embedding with a trained neural network encoder. The experiments show that these approaches are successful at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers.

Essay on "Sample Efficient Adaptive Text-to-Speech"

The paper "Sample Efficient Adaptive Text-to-Speech" introduces a notable advancement in the field of text-to-speech (TTS) systems through the employment of a meta-learning approach, aiming to achieve rapid adaptation to new speakers with minimal data. The paper stands on the premise that traditional TTS systems require extensive, high-quality datasets for training, particularly when attempting to synthesize the voices of new speakers. This dependency not only increases the cost associated with data acquisition but also limits the applicability of TTS systems in scenarios where such comprehensive datasets are impractical to procure.

The authors propose a multi-speaker model using a conditional WaveNet architecture, which serves as a shared core, supplemented by independent speaker-specific embeddings. The core aspect of this work is the design of a model that, rather than having fixed parameters post-training, can be rapidly adapted to novel input (new speaker voices) with very limited data. The paper explores three strategies for speaker adaptation:

1. Modifying only the speaker embeddings while keeping the WaveNet core parameters fixed.
2. Fine-tuning the entire model using stochastic gradient descent.
3. Predicting the speaker embeddings through a trained neural network encoder.

The empirical results demonstrate that their approach yields state-of-the-art performance in terms of both sample naturalness and voice similarity, even when only a few minutes of audio data from new speakers is available. This is quantified through Mean Opinion Score (MOS) evaluations and tests involving a speaker verification system. The models exhibit robust performance across various datasets, including the LibriSpeech and VCTK corpora, showcasing the framework's adaptability to different recording conditions.

The theoretical implications of this research lie in the field of few-shot learning, a burgeoning area within machine learning that focuses on training models capable of extrapolating complex patterns from minimal information. By integrating this concept into TTS technologies, the paper highlights potential ways of reducing data dependency, thereby expanding the accessibility and utility of TTS systems. Future developments could explore further enhancements in model architectures to minimize data requirements even more or enhance the quality and accuracy of generated speech with increasingly less information.

Practically, the work presents significant benefits, particularly for applications like personalized speech synthesis for individuals with voice impairments, where extensive voice data may not be available. Furthermore, since the model is designed for rapid adaptation, it could be effectively integrated into systems requiring real-time voice synthesis and adaption.

In conclusion, this paper contributes a substantial evolution in TTS systems through innovative meta-learning applications, successfully achieving quick adaptation to new speakers with scarce data. This not only underscores the potential for reduced data dependency in future TTS technologies but also paves the way for more versatile and user-specific voice synthesis applications. Further investigations in this direction hold promise for even greater advancements in the capabilities and applicability of TTS systems.