Detecting pathological speech using adaptive chaos exponent
Detecting pathological speech using adaptive chaos exponent
- Author(s): Jun He ; Yan-Xiong Li ; Qian-Hua He ; Fen Chen ; Xue-Yuan Zhang
- DOI: 10.1049/cp.2011.0842
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2011) — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): Jun He ; Yan-Xiong Li ; Qian-Hua He ; Fen Chen ; Xue-Yuan Zhang Source: IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2011), 2011 p. 33 – 37
- Conference: IET International Communication Conference on Wireless Mobile and Computing (CCWMC 2011)
- DOI: 10.1049/cp.2011.0842
- ISBN: 978-1-84919-505-8
- Location: Shanghai, China
- Conference date: 14-16 Nov. 2011
- Format: PDF
This paper proposes an algorithm to detect pathological speech using adaptive exponent of chaos. Within the proper rang of sampling delay, the optimal Correlation Dimension (CD) parameters is searched with the aim of obtaining the minimum Equal Error Rate (EER) without pre-setting optional sampling delay or the range of optional embedded CD. In the saturation of CD curve, the CD curve is split into sub-curves consisting of five successive elements, and the differences between any two adjacent elements in the sub-curve are calculated. Then, the subset possessing the minimum differences is regarded as the stable sub-curve of the CD curves, and the third element of the stable subset is regarded as the optimal CD. Finally, after the EER analysis of the training data, the CD and its corresponding sampling delay which possesses the minimum EER are chosen as the parameters of chaos. The experimental results show that the proposed algorithm possesses the Classification Correct Rate (CCR) of 75.3%. Compared with the Shimmer algorithm, the Jitter algorithm, the SHR algorithm and FZA algorithm, 9%, 20.1%, 19.1% and 15.5% of improvements in CCR are respectively obtained.
Inspec keywords: error statistics; speech processing
Subjects: Speech and audio signal processing; Other topics in statistics; Speech processing techniques; Other topics in statistics
Related content
content/conferences/10.1049/cp.2011.0842
pub_keyword,iet_inspecKeyword,pub_concept
6
6