文章基本信息

标题：Performance Evaluation of Loss Functions for Margin Based Robust Speech Recognition
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作者：Syed Abbas Ali ; Maria Andleeb ; Raheela Asif 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：2
DOI：10.14569/IJACSA.2016.070249
出版社：Science and Information Society (SAI)
摘要：Margin-based model estimation methods are applied for speech recognition to enhance the generalization capability of acoustic model by increasing the margin. An important aspects of margin based acoustic model for parameter estimation is that, the acoustic models are derived from soft margin concept and hinge loss function used in SVM as loss function to attained enhanced speech recognition performance. In this study, performance evaluation of loss functions (Logistic, Savage, Sigmoid) have been computed in the presence of white noise, pink noise, and brown noise with and without SVM classifiers to analyze the impact of noise on loss functions in comparison with hinge loss function used in SVM for parameter estimation in margin based acoustic model. Experimental results show that hinge loss function in the presence of pink noise and white noise have significant effects on isolated digits (0-9) in both pre-conditioned and recorded data samples in comparison with brown noise. Whereas hinge loss functions show serious anomalies with savage loss and sigmoid loss in term of performance and sigmoid loss function provides exceptionally good results in term of percentage error for all prescribed conditions.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Loss Functions; Statistical Learning; Automatic Speech Recognition (ASR); SVM Classifiers; Soft Margin Estimation (SME)