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

此应用程序已违反系统完整性，将被终止。 退出所有应用程序，退出Windows，然后重新启动计算机。由于执行特权指令，此应用程序违反了系统完整性。 本研究的目的是开发高效的非特定人连续语音识别自适应机制，以提高其鲁棒性下的广泛的扬声器和环境条件。自适应是基于语音频谱变化smurces的建模，是通过连续和迭代的语音模型参数的在线语音数据的无监督学习。该自适应还通过双通道语音/声音采集和通过串扰通道特性的估计来处理包括“鸡尾酒会”语音的干扰声音信号。语音建模和自适应在隐马尔可夫模型和其他统计算法的统计框架内。 讨论了自适应算法的收敛条件和快速实现方法。 自适应非特定人连续语音识别系统将在人机交互环境中进行研究，例如伊利诺斯大学贝克曼研究所的虚拟再现和交互式三维视觉计算。这笔资金将用于资助两名博士生。 该研究将为多语音变异源和干扰声下的鲁棒语音建模提供新的知识，显著提高非特定人连续语音识别系统的鲁棒性，并为非特定人连续语音识别系统在人机交互中的更广泛应用提供便利。

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.