文章基本信息

标题：Phonetic Distance Based Accent Classifier to Identify Pronunciation Variants and OOV Words
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作者：Akella Amarendra Babu ; Ramadevi Yellasiri ; Akepogu Ananda Rao 等
期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2015
卷号：6
期号：4
页码：33
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The state-of-the-art Automatic Speech Recognition (ASR) systems lack the ability to identify spoken wordsif they have non-standard pronunciations. In this paper, we present a new classification algorithm toidentify pronunciation variants. It uses Dynamic Phone Warping (DPW) technique to compute thepronunciation-by-pronunciation phonetic distance and a threshold critical distance criterion for theclassification. The proposed method consists of two steps; a training step to estimate a critical distanceparameter using transcribed data and in the second step, use this critical distance criterion to classify theinput utterances into the pronunciation variants and OOV words.The algorithm is implemented using Java language. The classifier is trained on data sets from TIMITspeech corpus and CMU pronunciation dictionary. The confusion matrix and precision, recall andaccuracy performance metrics are used for the performance evaluation. Experimental results showsignificant performance improvement over the existing classifiers.
关键词：Dynamic Phone Warping; Critical Phonetic Distance; machine Learning; Pronunciation variants