ITR-(NHS+ASE)-(int+dmc+sim) Automatic Speech Attribute Transcription (ASAT): A Collaborative Speech Research Paradigm and Cyberinfrastructure with Applications to Automatic Speech

长期以来，人们一直认为，人类根据存在于从声学到语用学的语音知识层次结构的各个级别上的检测到的证据来确定声音的语言身份。实际上，人们并不像自动语音识别（ASR）系统试图做的那样连续地将语音信号转换成单词。 相反，他们检测声音和听觉证据，权衡它们并将它们联合收割机形成认知假设，然后验证假设，直到达成一致的决定。 上述基于人类的语音处理模型为开发下一代语音技术提供了一个候选框架，该框架具有超越当前限制的潜力。为了弥合ASR系统和人类之间的性能差距，必须扩展ASR中语音到文本的狭义概念，以包含“隐藏”在语音话语中的所有相关人类信息。 而不是传统的自上而下，网络解码范式的ASR，我们正在建立一个自下而上，事件检测和证据组合的语音研究范式，以促进协作自动语音属性转录（ASAT）。 拟议项目的目标是：（1）开发特征探测和知识整合模块，以演示反卫星武器和自动搜索和检索;（2）建立一个开放源码、高度共享、即插即用的反卫星武器网络基础设施，用于合作研究，以降低自动搜索和检索的进入壁垒;（3）提供一种客观的评价方法，以监测各个模块和整个系统的技术进步。

It has long been postulated that a human determines the linguistic identity of a sound based on detected evidences that exist at various levels of the speech knowledge hierarchy, from acoustics to pragmatics. Indeed, people do not continuously convert a speech signal into words as an automatic speech recognition (ASR) system attempts to do. Instead, they detect acoustic and auditory evidences, weigh them and combine them to form cognitive hypotheses, and then validate the hypotheses until consistent decisions are reached. The above human-based model of speech processing suggests a candidate framework for developing next generation speech technologies that have the potential to go beyond the current limitations.In order to bridge the performance gap between ASR systems and humans, the narrow notion of speech-to-text in ASR has to be expanded to incorporate all related human information "hidden" in speech utterances. Instead of the conventional top-down, network decoding paradigm for ASR, we are establishing a bottom-up, event detection and evidence combination paradigm for speech research to facilitate collaborative Automatic Speech Attribute Transcription (ASAT). The goals of the proposed project are: (1) develop feature detection and knowledge integration modules to demonstrate ASAT and ASR; (2) build an open source, highly shared, plug-'n'-play ASAT cyberinfrastructure for collaborative research to lower entry barriers to ASR; and (3) provide an objective evaluation methodology to monitor technology advances in individual modules and across the entire system.