CAREER: Adaptive and Robust Automatic Speech Recognition inHuman-Computer Interaction

职业：人机交互中的自适应和鲁棒自动语音识别

• 批准号: 9502074
• 负责人: Yunxin Zhao
• 金额: $ 14.95万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-06-01 至 1998-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9502074&HistoricalAwards=false
• 关键词: CAREER; Adaptive; Robust; Automatic; Speech

This application has violated system integrity and will be terminated. Quit all applications, quit Windows, and then restart your computer. This application has violated system integrity due to execution of a privileged instruct. This research is aimed at developing highly effective adaptation mechanisms for speaker-independent continuous speech recognition so as to enhance its robustness under a wide range of speaker and environment conditions. The adaptation is based on the modeling of speech spectral variation smurces, and is via sequential and iterative unsupervised learning of speech model parameters from on-line speech data. The adaptation further handles interference sound signals including `cocktail party` speech via two-channel speech/sound acquisition, and via estimation of the cross-talk channel characteristics. The speech modeling and adaptation are within the statistical framework of the hidden Markov models and other statistical algorithms. The convergence condition and fast implementation of the adaptation algorithms are addressed. Adaptive speaker-independent, continuous speech recognition systems are to be studied in human-computer interaction contexts such as virtual renvironments and interactive three-dimensional visual computing at the Beckman Institute of the University of Illinois. The funding will be used to support two PhD students. This research is expected to contribute new knowledge to robust speech modeling in the presence of multiple speech variation sources and interference sounds, to significantly advance the robustness of speaker- independent, continuous speech recognition systems, and to facilitate a much wider scope of applications for speaker-independent, continuous speech recognition systemsin human-computer interaction.

该应用程序违反了系统的完整性，并将终止。 退出所有应用程序，退出Windows，然后重新启动计算机。由于执行特权指示，该应用程序违反了系统的完整性。 这项研究旨在为无关的连续语音识别开发高效的适应机制，以增强其在广泛的扬声器和环境条件下的鲁棒性。适应性基于语音频谱变化弹力的建模，并且是通过在线语音数据中的顺序且迭代的无监督学习语音模型参数的学习。适应进一步处理了通过两通道语音/声音获取以及通过互相访问频道特征的估计来处理干涉声音信号，包括“鸡尾酒会”演讲。语音建模和适应性在隐藏的马尔可夫模型和其他统计算法的统计框架内。 解决了适应算法的收敛条件和快速实现。 伊利诺伊州贝克曼学院（Beckman Institute of Huptual Renvironments）等人类交互环境（例如虚拟统一环境和互动式三维视觉计算），应在人类计算机的互动环境中进行研究。资金将用于支持两名博士生。 预计这项研究将在有多种语音变化来源和干扰声音的情况下为强大的语音建模提供新知识，从而显着提高说话者独立，连续的语音识别系统的鲁棒性，并促进较广泛的说话者应用程序的范围。 - 独立的，连续的语音识别系统在人类计算机相互作用中。