RI: Small: Ensemble Modeling of Speech Signals for Automatic Speech Recognition

RI：小型：用于自动语音识别的语音信号集成建模

• 批准号: 0916639
• 负责人: Yunxin Zhao
• 金额: $ 10万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916639&HistoricalAwards=false
• 关键词: RI; Small; Ensemble; Modeling; Speech

This project is aimed at developing a new framework of ensemble modeling of speech signals to address the long standing challenge of robust and accurate recognition of spontaneous speech. Toward this goal, random forests based allophonic clustering is used to construct ensemble models of allophones by random sampling the variables underpinning the allophonic variations; data sampling is used to enrich the diversity of the ensemble models by balancing within-set data sufficiency and between-sets data diversity; functional discriminative training is used to further optimize the efficiency and accuracy of the ensemble models. Experimental evaluations of these methods are performed on a standard speech recognition task to facilitate direct assessments of their efficacy by the speech research community. The ensemble modeling approach promises higher accuracy performance and lower computation costs than the current multiple system integration approach, owing to the improved likelihood scores contributed by the ensemble models in local steps of decoding search. The approach as advocated in this project opens up a new paradigm for investigating the many issues in speech acoustic modeling, it offers a new way for ensemble modeling of structured data generally, and therefore it has the potential of significantly impacting the fields of speech recognition and other machine learning applications. The research findings are disseminated via journal publication, conference presentation, and a website. The methods of this project have broad applications in speech recognition and structured data classification, and particularly they are employed to improve the accuracy performance of a telemedicine automatic captioning system.

该项目旨在开发一个新的语音信号建模框架，以应对强大而准确地认识自发语音的长期挑战。为了实现这一目标，基于森林的同种异体聚类用于通过随机采样同种异体变化的变量来构建异质的集成模型；数据采样用于通过平衡集合数据充足性和集合数据之间的数据多样性来丰富集成模型的多样性；功能判别训练用于进一步优化集合模型的效率和准确性。这些方法的实验评估是在标准语音识别任务上进行的，以促进语音研究界对其有效性的直接评估。合奏建模方法有望比当前的多个系统集成方法更高的准确性性能和更低的计算成本，这是因为在解码搜索的本地步骤中，集合模型的可能性提高了。 该项目中提倡的方法为调查语音声学建模的许多问题开辟了一个新的范式，它为整体建模提供了总体结构化数据的新方法，因此它具有显着影响语音识别和其他机器学习应用领域的潜力。研究发现是通过期刊出版物，会议演示和网站传播的。 该项目的方法在语音识别和结构化数据分类中具有广泛的应用，尤其是使用它们来提高远程医疗自动字幕系统的准确性性能。