非表示:
キーワード:
speech processing, clustering methods, unsupervised
learning.
要旨:
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.
