Mathematical Sciences: Proposal on Feature Extraction in Spoken Language Acquisition

数学科学：口语习得中特征提取的建议

• 批准号: 9407050
• 负责人: Isaac Chavel
• 金额: $ 7.1万
• 依托单位: CUNY Graduate School University Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 1997-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9407050&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Proposal; Feature; Extraction

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.

小行星9407050 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， 第一种是将口语转录成文本，第二种是从转录的语音中推导出意义。 这些任务传统上是单独处理的，假设在转录层面有一个干净的界面。 然而，很明显，声学和语义问题必须密切相关。 人们还认为，基于傅立叶频谱的语音信号的声学分析的传统工具对于流利语音的鲁棒识别是不够的。 我们的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，提高口语理解系统的性能为目标。 具体来说，我们的目的是整合的时间-频率技术正在开发的纽约市立大学现有的声学和语义处理已经在AT T贝尔实验室。 一个主要的问题将是理解提取的时间-频率对象与传统的语音子字单元的关系，重点是在语义模糊的区域中提供改进的声学区分。 虽然由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。