.png){kind=icon}
返回
A segmental-feature HMM for speech pattern modeling
.png){kind=icon}
PRE
AI
DOI：10.1109/97.844631.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this letter, we propose a nea trajectory model for characterizing segmental features and their interaction based upon a general framework of hidden Markov models. Each segment, a sequence of frame vectors, is represented by a trajectory of observed vector sequences. This trajectory replaces the frame features in the segment and becomes the input of the segmental hidden Markov models (HMM's), In our approach, we adopt polynomial trajectory modeling to represent the trajectories using a new design matrix that includes transitional information on neighborhood acoustic events. To apply this trajectory to the segmental HMM, extra- and intrasegmental variations are modified to contain trajectory information. The presented model is regarded as an extension and generalization of conventional HMM, trajectory-based segmental HMM, and parametric trajectory models. The experimental results are reported on the TIMIT corpus and performance is shown to improve significantly over that of the conventional HMM.
Keyword:
parametric trajectory model
segmental feature HMM
segmental HMM
speech recognition
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
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
The Influence of Bromfenac on the Pharmacokinetics and Pharmacodynamic Responses to Glyburide in Diabetic Subjects布洛芬那卡对糖尿病受试者格列本脲药代动力学和药效学反应的影响
PREAI
分享收藏
没有更多内容
没有更多内容