Mathematical Sciences: Proposal on Feature Extraction in Spoken Language Acquisition

9407050澳大利亚人自动语音理解有很多方面，但总体问题通常分为两个主要任务，第一个是将口语转录成文本，第二个是从转录的语音中推导出意义。这些任务传统上是分开处理的，假设在转录水平上有一个干净的界面。然而，很明显，声学和语义问题必须密切相关。传统的基于傅立叶谱的语音信号声学分析工具不足以对流利的语音进行稳健的识别。我们项目的实质是研究时频特征提取技术，以澄清声学-语义关系，目标是提高口语理解系统的性能。具体地说，我们的目标是将CUNY正在开发的时频技术与AT&&T贝尔实验室已经到位的现有声学和语义处理相结合。主要的问题将是理解提取的时频对象与传统语音子词单元的关系，重点是在语义歧义区域中提供改进的声学辨别。虽然听觉外周提取的主要特征是人类与生俱来的，但知觉度量、子词和单词符号是后天知识。我们想要了解时频特征提取技术如何在机器中模拟这种行为，以提高口语处理的稳健性。

9407050 Auslander There are many ascpects of automated speech understanding, but the overall problem is often divided into two main tasks, The first is the transcription of spoken language into text and the second is the derivation of meaning from the transcribed speech. These tasks are traditionally addressed separately, assuming a clean interface at the level of transcription. However, it is clear that the acoustic and semantic problems must be intimately related. It is also felt that traditional tools of acoustic analysis of speech signals, based on the Fourier spectrum, are insufficient for the robust recognition of fluent speech. The substance of our project will be to study time-frequency feature extraction techniques for the purpose of clarifying the acoustic-semantic relationship, with the goal of improving the performance of systems for spoken language understanding. Specifically, we purpose the integration of time-frequency techniques being developed at CUNY with existing acoustic and semantic processing already in place at AT&T Bell Laboratories. A primary issue will be to understand the relationship of the extracted time-frequency objects to traditional phonetic subword units, with emphasis on providing improved acoustic discrimination in regions of semantic ambiguity. While primary features extracted by the auditory periphery are innate in humans, the perceptual metric, subword and word symbols are acquired knowledge. We want to understand how time-frequency feature extraction techniques might simulate this behavior in a machine, for the purpose of improving the robustness of spoken language processing.