UNSUPERVISED LEARNING ALGORITHM FOR VOWEL TEMPLATES BASED ON MINIMUM QUANTIZATION DISTORTION.

被引：0
|
作者：
Sugiyama, Masahide
Shikano, Kiyohiro
机构：
来源：
Reports of the Electrical Communication Laboratory | 1986年 / 34卷 / 03期
关键词：
MATHEMATICAL TECHNIQUES - Algorithms - SYSTEMS SCIENCE AND CYBERNETICS - Learning Systems;
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摘要：
A new unsupervised learning algorithm is proposed for speaker-independent speech recognition. The algorithm is formulated as a problem involving selection of an optimum vowel template set from pre-stored sets based on minimum quantization distortion. This algorithm has two advantages: learning is unsupervised, and learning and recognition are accomplished simultaneously. To evaluate this algorithm, word recognition experiments focusing upon 100 city names were carried out for 30 males and 20 females.
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页码：357 / 362
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