Mathematical Sciences: Proposal on Feature Extraction in Spoken Language Acquisition

语音自动理解有很多方面，但整体问题往往分为两个主要任务，第一是将口语转录成文本，第二是从转录的语音中衍生出意义。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接口。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语音进行鲁棒识别。我们项目的实质是研究时频特征提取技术，目的是澄清声学-语义关系，以提高口语理解系统的性能。具体来说，我们的目标是将纽约市立大学正在开发的时频技术与贝尔实验室现有的声学和语义处理技术相结合。一个主要问题将是理解提取的时频对象与传统语音子词单位的关系，重点是在语义模糊区域提供改进的声学识别。虽然听觉外围提取的主要特征是人类天生的，但感知度量、子词和词符号是后天获得的知识。我们想了解时频特征提取技术如何在机器中模拟这种行为，以提高口语处理的鲁棒性。

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.