Building a Speech Corpus for Prompt-Based Voice Control

核心概念

Enhancing text-to-speech synthesis by controlling voice characteristics through prompt-based methods.

摘要

The content discusses the creation of a novel corpus to manipulate voice characteristics in text-to-speech synthesis. It outlines the methodology for building the corpus, filtering data, ensuring quality, and manual annotation. The paper also proposes a model for retrieving speech from voice characteristics descriptions.

Structure:

1. Introduction to Speech Production and TTS Challenges
2. Methodology for Corpus Construction
3. Quality Assurance Processes
4. Manual Annotation of Voice Characteristics Descriptions
5. Analysis of Corpus Diversity and Linguistic Features
6. Training Algorithm for Model Retrieval of Speech from Descriptions

統計資料

"We construct an open corpus, Coco-Nut1."
"The corpus consists of 54,610 speech segments from 1,523 videos."
"The MLM scores of collected segments are distributed around the peak of -3."

引述

"We propose a method to construct a paired corpus of speech and voice characteristics descriptions with a broad range of voice characteristics."
"We open-sourced the corpus, which is the first corpus that covers diverse in-the-wild voice characteristics."

從以下內容提煉的關鍵洞見

Building speech corpus with diverse voice characteristics for its prompt-based representation

by Aya Watanabe... 於 arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13353.pdf

深入探究

How can this novel corpus impact future developments in text-to-speech technology?

The novel corpus developed in this study, which pairs speech samples with voice characteristics descriptions, has the potential to significantly impact future developments in text-to-speech (TTS) technology. By providing a diverse range of voice characteristics data sourced from the Internet, this corpus enables researchers and developers to train TTS models that can mimic various speaking styles, emotions, and personalities. This diversity allows for more nuanced control over voice characteristics in synthesized speech, leading to more natural and expressive TTS output.
Furthermore, the availability of such a comprehensive corpus facilitates research into prompt-based TTS systems. These systems rely on free-form descriptions of voice characteristics to generate speech corresponding to specific attributes like age, gender, tone, or emotion. The corpus serves as a valuable resource for training and evaluating these prompt-based TTS models by providing a wide array of voice samples paired with detailed descriptions.
In essence, this novel corpus opens up new possibilities for enhancing the quality and versatility of TTS technology by enabling better control over voice characteristics and improving the overall naturalness and expressiveness of synthesized speech.

How can challenges might arise when implementing prompt-based manipulation of voice characteristics on a larger scale?

Implementing prompt-based manipulation of voice characteristics on a larger scale may pose several challenges that need to be addressed:

Data Quality: As the scale increases, ensuring high-quality data becomes crucial. With a larger dataset encompassing diverse voices and attributes, maintaining consistency in data quality across all entries becomes challenging.

Annotation Consistency: Scaling up annotation efforts for describing voice characteristics requires meticulous attention to ensure consistency among annotators. Variability in annotations could lead to inaccuracies or biases in the training data.

Computational Resources: Handling large volumes of audio data along with associated text prompts necessitates significant computational resources for processing and analysis. Training complex models on extensive datasets requires robust infrastructure.

Model Generalization: Ensuring that prompt-based models generalize well across different languages or dialects is another challenge when scaling up operations globally. Adapting models trained on one language or culture to others while preserving accuracy is non-trivial.

Ethical Considerations: With increased scalability comes an amplified responsibility towards ethical considerations such as privacy protection when dealing with vast amounts of personal audio data sourced from the internet.

How can the proposed model be adapted for languages other than Japanese?

Adapting the proposed model for languages other than Japanese involves several key steps:

Language-specific Data Collection: Gather high-quality speech samples paired with corresponding voice characteristic descriptions from sources relevant to each target language.

2Translation: Translate both textual prompts describing vocal features into each target language as well as any transcriptions if necessary.
3Model Architecture Modification: Adjust pre-trained encoder-decoder architectures like RoBERTa or HuBERT used in training based on linguistic nuances specific to each language.
4Fine-tuning: Fine-tune hyperparameters such as learning rate or batch size accordingto linguistic properties unique toeachlanguageforoptimalperformance
5EvaluationandValidation: Evaluate themodel's performanceonthe newlanguagesetstoensureaccuratevoicecharacteristicsretrieval
By following these steps tailored specificallytothelinguisticandculturalcontextofeachtargetlanguage,the proposedmodelcanbeeffectivelyadaptedforprompt-basemanipulationofvoicecharacteristicsinavarietyoflanguagesbeyondJapanese