Application of wavelet transforms for C∕V segmentation on Mandarin speech signals

Chen, S.-H.; Wang, J.-F.

doi:10.1049/ip-vis:20010151

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2006

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is difficult to detect sudden burst in a slowly varying signal by FT. Recently, WT has been proposed for feature extraction [1][2][3]. To overcome the problem of fixed resolution extracted from FT, the WT uses adaptive window sizes, which allocate more time to the lower frequency and less time for the higher frequency [4,5].…”

Section: Introductionmentioning

confidence: 99%

Speech Classification Based on Fuzzy Adaptive Resonance Theory

Hsu¹,

Hsieh²

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

View full text Add to dashboard Cite

This paper presents a neuro-fuzzy system to speech classification. We propose a multi-resolution feature extraction technique to deal with adaptive frame size. We utilize fuzzy adaptive resonance theory (FART) to cluster each frame. FART was an extension to ART, performs clustering of its inputs via unsupervised learning. ART describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. In our experiments, the TIMIT database is used and extracts features of each phoneme. The performance of speech classification is 88.66%, demonstrate the effectiveness of the proposed system is encouraging.

show abstract

Section: Introductionmentioning

confidence: 99%