Title :
Finding Maximum Margin Segments in Speech
Author :
Estevan, Yago Pereiro ; Wan, Vincent ; Scharenborg, Odette
Author_Institution :
Dept. of Signal Theory & Commun., Universidad Carlos III de Madrid
Abstract :
Maximum margin clustering (MMC) is a relatively new and promising kernel method. In this paper, we apply MMC to the task of unsupervised speech segmentation. We present three automatic speech segmentation methods based on MMC, which are tested on TIMIT and evaluated on the level of phoneme boundary detection. The results show that MMC is highly competitive with existing unsupervised methods for the automatic detection of phoneme boundaries. Furthermore, initial analyses show that MMC is a promising method for the automatic detection of sub-phonetic information in the speech signal.
Keywords :
pattern clustering; speech processing; unsupervised learning; TIMIT; automatic detection; automatic speech segmentation methods; kernel method; maximum margin clustering; phoneme boundary detection; speech signal; subphonetic information; unsupervised speech segmentation; Automatic speech recognition; Automatic testing; Clustering methods; Computer science; Information analysis; Kernel; Signal analysis; Speech analysis; Speech processing; Support vector machines; clustering methods; speech processing; unsupervised learning;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0727-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2007.367225
