返回
Robust speech recognition and feature extraction using HMM2
DOI:10.1016/S0885-2308(03)00012-3.png)
摘要
En 中文
This paper presents the theoretical basis and preliminary experimental results of a new HMM model, referred to as HMM2, which can be considered as a mixture of HMMs. In this new model, the emission probabilities of the temporal (primary) HMM are estimated through secondary, state specific, HMMs working in the acoustic feature space. Thus, while the primary HMM is performing the usual time warping and integration, the secondary HMMs are responsible for extracting/modeling the possible feature dependencies, while performing frequency warping and integration. Such a model has several potential advantages, such as a more flexible modeling of the time/frequency structure of the speech signal. When working with spectral features, such a system can also perform nonlinear spectral warping, effectively implementing a form of nonlinear vocal tract normalization. Furthermore, it will be shown that HMM2 can be used to extract noise robust features, supposed to be related to formant regions, which can be used as extra features for traditional HMM recognizers to improve their performance. These issues are evaluated in the present paper, and different experimental results are reported on the Numbers95 database. (C) 2003 Elsevier Science Ltd. All rights reserved.
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
3.4
论文数:
1.5K
被引数:
2.6K
机构
暂无机构信息
引用论文
Effects of the type of polycation on the amperometric response of choline biosensors prepared by a layer-by-layer deposition technique聚阳离子类型对通过逐层沉积技术制备的胆碱生物传感器安培响应的影响
Clinical characterization and mutation spectrum in Hispanic families with adenomatous polyposis syndromes腺瘤性息肉综合征的西班牙裔家庭的临床特征及突变谱
没有更多内容

