| 配分額 *注記 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2023年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2022年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2021年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
|
|---|
| 研究開始時の研究の概要 |
Language technology has improved due to advances in neural-network-based approaches; for example, speech synthesis has reached the quality of human speech. However, neural models require large quantities of data. Speech technologies bring social benefits of accessibility and communication - to ensure broad access to these benefits, we consider language-independent methods that can make use of less data. We propose 1) articulatory class based end-to-end speech synthesis; 2) multi-modal machine translation with text and speech; and 3) neural architecture search for data-efficient architectures.
|
| 研究実績の概要 |
We developed methods for text-to-speech (TTS) synthesis for low-resource languages using smaller amounts of data as well as data from less traditional sources. First, we developed an approach to building text-to-speech (TTS) corpora from podcast data, using the Hebrew language as a case study, resulting in a publicly-available dataset. We next developed a data processing pipeline and TTS system that can be repurposed for other low-resource languages that have similar available data, resulting in one peer-reviewed publication at Interspeech 2023. Finally, we continued investigating self-supervised speech representations as an intermediate representation for multilingual TTS which can be fine-tuned to a new language.
Having previously identified automatic evaluation of TTS as a critical issue especially for low-resource languages, we continued the VoiceMOS Challenge, a shared task for automatic TTS evaluation, by running a second edition focusing on zero-shot multi-domain scenarios. The challenge was presented as a special session at ASRU 2023, and attracted ten teams from academia and industry. We also studied contextual effects on listener ratings, self-supervised speech models' abilities for speech quality prediction, and a ranking-based quality prediction approach, resulting in three additional peer-reviewed publications.
|
