Expanding Multilingual Speech Synthesis to 100+ Languages Using Untranscribed Data

Core Concepts

The author proposes a framework for scaling multilingual TTS models to over 100 languages using untranscribed data, achieving high intelligibility and naturalness scores with minimal supervision.

Abstract

The content discusses a novel framework for extending multilingual text-to-speech (TTS) systems to over 100 languages without transcribed data. By leveraging unsupervised training methods and joint speech-text representation learning, the proposed model can generate intelligible speech in unseen languages with minimal supervision. The study highlights the importance of found data sources, such as untranscribed speech and unspoken text, in expanding language coverage for TTS systems. Through a detailed experimental setting and results analysis, the effectiveness of the proposed framework is demonstrated in improving intelligibility and naturalness scores across various languages.
The paper introduces a comprehensive approach that combines supervised and unsupervised learning techniques to enhance TTS performance across multiple languages. By utilizing pretraining frameworks and joint speech-text models, the study showcases significant advancements in zero-shot multilingual text-to-speech synthesis. The evaluation metrics include character error rates (CER), mean opinion scores (MOS), and SQuId assessments to measure intelligibility and naturalness of synthesized speech. Ablation studies further validate the effectiveness of different components in improving TTS performance under varying data conditions.
Overall, the research presents a groundbreaking method for expanding multilingual TTS capabilities by leveraging unsupervised learning strategies and innovative training procedures. The findings demonstrate promising results in achieving high-quality speech synthesis across diverse languages with limited or no transcribed data available.

Stats

Without any transcribed speech in a new language, this TTS model can generate intelligible speech in ¿30 unseen languages.
With just 15 minutes of transcribed found data, we can reduce the intelligibility difference to 1% or less from the ground-truth.
The proposed model shows intelligibility differences of 1% or less when compared to ground-truth data in 30 languages.
The WaveFit vocoder is trained based on studio-quality American English speech.
The training steps for ASR model training were 200k steps with a batch size of 256.
For text MLM pretraining, the model was trained for 2M iterations with a batch size of 1,024.

Quotes

"Collecting high-quality studio recordings of audio is challenging."
"The proposed framework combines speech-text encoder pretraining with unsupervised training using untranscribed speech."
"Our main contributions are: A novel TTS framework using unsupervised joint speech-text learning."

Key Insights Distilled From

Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data

by Taka... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18932.pdf

Deeper Inquiries

How does leveraging found data impact the scalability of multilingual TTS systems

Leveraging found data significantly impacts the scalability of multilingual TTS systems by expanding language coverage without the need for transcribed data in each specific language. Found data, which includes untranscribed speech and unspoken text sources, allows for training models on a wider range of languages with minimal supervision. This approach enables TTS systems to be developed for low-resource languages where collecting high-quality transcribed audio data is challenging. By utilizing found data, the proposed framework in the context can scale a multilingual TTS model to over 100 languages, spanning various language families and writing systems. The ability to train on diverse found data sources enhances the adaptability and reach of multilingual speech synthesis systems.

What are potential challenges associated with relying on untranscribed data sources for training

Relying on untranscribed data sources for training poses several potential challenges in developing robust multilingual TTS systems:

Quality Control: Untranscribed speech may contain varied recording conditions, linguistic inconsistencies, imprecise pronunciation, or disfluencies that can impact model performance.
Data Variability: Lack of standardized transcription means dealing with variability in accents, dialects, background noise levels across different datasets.
Labeling Issues: Generating accurate pseudo-labels from untranscribed speech requires sophisticated algorithms or external ASR models which may introduce errors affecting model training.
Generalization Challenges: Models trained solely on untranscribed data might struggle with generalizing well to unseen languages due to limited linguistic patterns captured during training.

Addressing these challenges through innovative techniques such as joint speech-text unsupervised learning frameworks can help mitigate issues related to using untranscribed data effectively.

How might advancements in unsupervised learning techniques influence other areas beyond multilingual speech synthesis

Advancements in unsupervised learning techniques within the realm of multilingual speech synthesis have broader implications beyond just improving TTS systems:

Cross-Domain Applications: Techniques developed for unsupervised learning in TTS could be adapted and applied across other domains like natural language processing (NLP) or computer vision where labeled datasets are scarce.
Low-Resource Language Support: Improved unsupervised methods could aid in developing tools and technologies for preserving endangered languages or facilitating communication barriers by enabling efficient translation services.
Transfer Learning Benefits: Enhanced unsupervised learning approaches could enhance transfer learning capabilities across different tasks within machine learning paradigms leading to more efficient utilization of existing resources.
Innovation Catalyst: Progress in unsupervised techniques fosters innovation by pushing boundaries towards creating more adaptable AI models capable of self-learning from vast amounts of unlabeled or minimally labeled information.

The advancements made through research into unsupervised learning methodologies not only benefit specific applications like multilingual TTS but also contribute towards advancing AI capabilities more broadly across various disciplines and industries.