Prediction of Acoustic Feature Parameters Using Myoelectric Signals

被引:17
|
作者
Lee, Ki-Seung [1 ]
机构
[1] Konkuk Univ, Dept Elect Engn, Seoul 143701, South Korea
关键词
Minimum mean square error (MMSE) criterion; mutual information (MI); myoelectric signals (MES); speech synthesizer; SPEECH; CLASSIFICATION; NECK;
D O I
10.1109/TBME.2010.2041455
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
It is well-known that a clear relationship exists between human voices and myoelectric signals (MESs) from the area of the speaker's mouth. In this study, we utilized this information to implement a speech synthesis scheme in which MES alone was used to predict the parameters characterizing the vocal-tract transfer function of specific speech signals. Several feature parameters derived from MES were investigated to find the optimal feature for maximization of the mutual information between the acoustic and the MES features. After the optimal feature was determined, an estimation rule for the acoustic parameters was proposed, based on a minimum mean square error (MMSE) criterion. In a preliminary study, 60 isolated words were used for both objective and subjective evaluations. The results showed that the average Euclidean distance between the original and predicted acoustic parameters was reduced by about 30% compared with the average Euclidean distance of the original parameters. The intelligibility of the synthesized speech signals using the predicted features was also evaluated. A word-level identification ratio of 65.5% and a syllable-level identification ratio of 73% were obtained through a listening test.
引用
收藏
页码:1587 / 1595
页数:9
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