SGER: Exploring New Auditory Perception Based Approaches to ASR

在过去的二十年里，自动语音识别（ASR）已经使用一种模式匹配范式来解决，该范式将声学、发音和语言模型自上而下地表征为一个集成的有限状态网络。虽然多年来取得了重大进展，但最近进展开始放缓；然而，目前的状态还没有匹敌人类语音识别能力。这主要是由于：(1)无法以自上而下的方式获得解决ASR问题所需的所有知识来源的完整规范，以及(2)ASR的鲁棒性问题。该项目正在开发和评估一种基于知识和数据驱动的ASR听觉感知方法。通过将ASR问题分解为语音信号中声学和语音标记的检测，然后是一系列频谱和时间知识整合阶段，所提出的方法模仿了人类听觉感知过程，同时保留了语音和语言随机建模的许多关键特征，这些特征有助于当前流行的模式匹配方法的成功。该项目正在研究作为该方法基础的特征检测和知识集成算法的可行性，并正在创建一个即插即用平台，以促进广泛的语音科学和语音处理社区之间的合作。这项研究将有助于更好地理解听觉感知与ASR之间的联系，为学生和研究人员更好地理解语音基础提供教育机会，挑战信号处理社区开发新的语音特征检测算法，并为促进协同ASR研究的通用软件和评估平台铺平道路。

In the last two decades, automatic speech recognition (ASR) has been addressed using a pattern matching paradigm with a top-down characterization of acoustic, pronunciation, and language models as an integrated finite state network. Although there have been significant advances over the years, the progress has begun to slow recently; however, the current state-of-the-state has yet to rival human speech recognition capability. This is mainly due to: (1) the inability to obtain a complete specification of all the knowledge sources needed to solve the ASR problem in a top-down manner, and (2) the ASR robustness problem. This project is developing and evaluating an auditory perception approach to ASR that is both knowledge-based and data-driven. By decomposing the ASR problem into detection of acoustic and phonetic landmarks in the speech signal followed by a sequence of spectral and temporal knowledge integration stages, the proposed approach mimics the human auditory perception process while retaining many of the key features in stochastic modeling of speech and language that have contributed to the success of the currently prevailing pattern matching approaches. The project is investigating the feasibility of feature detection and knowledge integration algorithms that are foundational to the approach and is creating a plug-and-play platform to facilitate cooperation between broad speech science and speech processing communities. This research should facilitate a better understanding of the link between auditory perception and ASR, provide educational opportunities to students and researchers to better understand speech fundamentals, challenge the signal processing community to develop new speech feature detection algorithms, and pave the way for a common software and evaluation platform to facilitate collaborative ASR research.