EAGER: Linguistic Event Extraction and Integration (LEXI): A New Approach to Speech Analysis

这一探索性项目开发了一种新的语音信号分析系统，可用于改进自动语音识别(ASR)系统，并提供可测试的人类语音感知模型。该系统基于发现语音信号中的重要事件，即，由于在形成辅音(如/p/或/S/)或元音(如/a/或/u/)期间嘴巴闭合或张开而使信号突然改变的‘声学边缘’。这些突如其来的变化被称为地标，特别具有信息性，因为它们(以及它们附近的信号部分)对说话人的意图及其声音提供了丰富的信息。通过识别语音信号中的语言相关信息，而不是测量信号的每个部分，关注这些事件导致更高的计算效率。这种对语音个体提示的关注也意味着该系统可以处理儿童、老年人、有外国口音的人或有临床语言障碍的人产生的非典型语音。因此，这一系统将把ASR的好处带给那些目前识别系统没有很好服务的扬声器，让更多的人使用手机、平板电脑和笔记本电脑成为可能。虽然现有的系统通过对典型语音的大样本进行统计分析，对于典型说话者来说工作得很好，但它们让许多人得不到服务。基于Landmark的系统还将提供一个工具，用于测试人类语音识别是否依赖于找到单词声音的个别提示，即使这些提示在不同的上下文中非常不同，因此可以导致开发一种新的人类语音感知模型。该系统的工作原理是从信号中提取与语音相关的测量，如基频、共振峰频率、频谱能量及其导数，并将这些测量解释为不同特征的声学提示。该系统的创新方面包括使用地标，这是最健壮的声学特征提示，并与发音方式特征相关。一旦找到标志性的声学线索，也可以找到与位置和发声特征以及韵律结构相关的其他声学线索。独特特征和韵律结构的提取提供了可以从物理连续信号中提取的第一个抽象语言单位，并且该信息被用于识别单词，并构建整个话语的表示。为了开发和评估这一创新系统的性能，将使用Landmark和其他声学提示手动标记由孤立的元音-辅音-元音序列、阅读连续语音、阅读无线电风格的语音和自发语音组成的语音数据库。这一基础语音研究项目的结果将支持ASR新方法的开发，将提供一个可测试的人类语音产生的计算模型，并将产生适合于开发教程的材料，以培训工程、语言学和认知科学的学生标记声学特征线索。

This exploratory project develops a new system for speech signal analysis that can be used to improve automatic speech recognition (ASR) systems, and provide a testable model of human speech perception. The system is based on finding important events in the speech signal, i.e. 'acoustic edges' where the signal changes suddenly because the mouth closes or opens during the formation of a consonant (like /p/ or /s/), or a vowel (like /a/ or /u/). These abrupt changes, called Landmarks, are especially informative, because they (and the parts of the signal near them) are richly informative about the speaker's intended words and their sounds. Focusing on these events results in greater computational efficiency, by identifying the linguistically relevant information in the speech signal, rather than measuring every part of the signal. This focus on individual cues to speech sounds also means that the system can deal with non-typical speech produced by children, older people, speakers with foreign accents, or those with clinical speech disabilities. As a result, this system will bring the benefits of ASR to speakers who are not well served by current recognition systems, making it possible for more people to use cell phones, tablets and laptops. While existing systems work well for typical speakers by using statistical analysis of large samples of typical speech, they leave many people underserved. The Landmark-based system will also provide a tool for testing whether human speech recognition depends on finding the individual cues to the sounds of words, even when those cues are very different in different contexts, and so can lead to the development of a new model of human speech perception.The system works by extracting speech-related measurements from the signal, such as fundamental frequency, formant frequencies, spectral band energies and their derivatives, and interpreting these measures as acoustic cues for distinctive features. Innovative aspects of the system include the use of Landmarks, which are the most robust of the acoustic feature cues and are related to articulatory manner features. Once the landmark acoustic cues are found, other acoustic cues related to place and voicing features, and to prosodic structure, can also be found. The extraction of distinctive features and prosodic structure provides the first abstract linguistic units that can be extracted from the physical continuous signal, and this information is used to identify words, and to construct a representation of the entire utterance. To develop and evaluate the performance of this innovative system, speech databases consisting of isolated vowel-consonant-vowel sequences, read continuous speech, read radio-style speech, and spontaneous speech will be hand-labeled with Landmarks and other acoustic cues. Results of this basic speech research project will support the development of new approaches to ASR, will provide a testable computational model of human speech production, and will produce material suitable for development of a tutorial to train students in engineering, linguistics and cognitive science to label acoustic feature cues.