Knowledge-Based Analysis of Speech Mixed with Steady and Sporadic Environmental Sounds

这是一个探索性研究（SGER）项目。 的 该项目调查了利用知识的可行性 稳定和零星的背景噪声源，发生在 不适合语音识别的任意环境， 将它们与电话扬声器独立清晰度隔离 在识别任务中的数字。 环境噪声背景 可以在家里，办公室和汽车中找到; 稳态噪声包括源自空气噪声 空调或汽车发动机，而零星的噪音源 是离散的事件，如一个人走，一扇门砰地关上， 电话铃响，或电器被打开。问题是 的基础上解决的信号理解体系结构 具有用于特征提取的上下文相关的重新配置 过程 第一轮特征提取的结果 的时间-频率区域进行了分析， 系统关于各种语音的相关性的知识， 非言语来源。 那些确定性更高的地区--或岛屿 的确定性-被认为是在预测潜在的 可能出现在其他部分的非语音源的掩蔽语音 的话语。 这些区域也被用作 特征提取过程的重新配置， 分析掩蔽区域以及验证、修剪和 在所有地区完善假设。 总而言之，虽然 系统要识别语音，它还必须执行 非语音识别，帮助语音识别 过程 //

This is a Small Grant for Exploratory Research (SGER) project. The project investigates the feasibility of using knowledge from the steady and sporadic background noise sources that occur in arbitrary environments not conditioned for speech recognition to isolate them from speaker-independent articulation of telephone numbers in a recognition task. The environmental noisy background could be found in home, offices, and automobiles; the types of steady-state noise include those originating from an air conditioner or from a car engine, while the sporadic noise sources are discrete events such as a person walking, a door slammed, a ringing telephone, or an appliance being switched on. The problem is addressed on the basis of a signal-understanding architecture with context-dependent reconfiguration for the feature extraction process. The results from an initial round of feature extraction are analyzed for time-frequency regions consistent with the system's knowledge regarding the correlates of various speech and non-speech sources. Those regions of higher certainty - or islands of certainty - are considered in the prediction of potentially masked speech by non-speech sources that may occur in other parts of the utterance. Those regions are used also as a basis in the reconfiguration of the feature-extraction process which is used in analyzing the masked region as well as in verifying, pruning, and refining hypotheses in all regions. In summary, while the goal of the system is to recognize speech, it must also perform the recognition on non-speech sounds to help the speech recognition process. //