Improved HMM/SVM methods for automatic phoneme segmentation
Improved HMM/SVM methods for automatic phoneme segmentation
复制标题
改进的 HMM/SVM 方法用于自动音素分割
DOI：
10.21437/interspeech.2007-557
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
H. Wang
中科院分区：
文献类型：
--
作者：
Jen;Hung;H. Wang
本文提出了一种改进的HMM/SVM两阶段音素分割框架，试图模仿人类音素分割过程。第一阶段根据最小边界误差（MBE）准则执行隐马尔可夫模型（HMM）强制对齐。我们的目标是对齐的语音发音的音素序列与其声学信号对应的MBE训练的HALTH和显式音素持续时间模型的基础上。第二阶段使用支持向量机（SVM）的方法来细化假设的音素边界推导出基于HMM的强制对齐。该框架的有效性已被验证的两个语音数据库：TIMIT英语数据库和MATBN汉语普通话数据库。
This paper presents improved HMM/SVM methods for a twostage phoneme segmentation framework, which tries to imitate the human phoneme segmentation process. The first stage performs hidden Markov model (HMM) forced alignment according to the minimum boundary error (MBE) criterion. The objective is to align a phoneme sequence of a speech utterance with its acoustic signal counterpart based on MBE-trained HMMs and explicit phoneme duration models. The second stage uses the support vector machine (SVM) method to refine the hypothesized phoneme boundaries derived by HMM-based forced alignment. The efficacy of the proposed framework has been validated on two speech databases: the TIMIT English database and the MATBN Mandarin Chinese database.