Subband centroids and deep neural networks for robust speech recognition

用于稳健语音识别的子带质心和深度神经网络

• 批准号: DP170102907
• 负责人: Prof Kuldip Paliwal
• 金额: $ 22.56万
• 依托单位: Griffith University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2017
• 资助国家: 澳大利亚
• 起止时间: 2017-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP170102907
• 关键词: Subband; centroids; deep; neural; networks

This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make the speech and speaker recognition systems less sensitive to additive background noise and make them more useful in telecommunications and business.

本课题旨在提高自动语音和说话人识别系统的鲁棒性和准确性。尽管这些系统在无噪声环境中工作得相当好，但即使存在少量噪声，它们的性能也会急剧下降。为了克服这个问题，本项目提出了一种缺失特征的鲁棒语音和说话人识别方法。这种方法有望使语音和说话人识别系统对附加的背景噪声不那么敏感，并使它们在电信和商业中更有用。