Acoustic Feature Transformation Combining Average and Maximum Classification Error Minimization Criteria
نویسندگان
چکیده
منابع مشابه
Acoustic Feature Transformation Combining Average and Maximum Classification Error Minimization Criteria
Acoustic feature transformation is widely used to reduce dimensionality and improve speech recognition performance. In this letter we focus on dimensionality reduction methods that minimize the average classification error. Unfortunately, minimization of the average classification error may cause considerable overlaps between distributions of some classes. To mitigate risks of considerable over...
متن کاملAcoustic Feature Transformation Based on Generalized Criteria for Speech Recognition
This thesis deals with acoustic feature transformations in automatic speech recognition to improve basic performance of a speech recognizer. The aim of acoustic feature transformations is to reduce dimensionality of long-term speech features without losing discriminative information among the different phonetic classes. First, we focus on optimizing acoustic feature transformations using criter...
متن کاملFeature Selection by Nonparametric Bayes Error Minimization
This paper presents an algorithmic framework for feature selection, which selects a subset of features by minimizing the nonparametric Bayes error. A set of existing algorithms as well as new ones can be derived naturally from this framework. For example, we show that the Relief algorithm greedily attempts to minimize the Bayes error estimated by k-Nearest-Neighbor method. This new interpretati...
متن کاملCombining feature spaces for classification
Article history: Received 2 November 2007 Received in revised form 24 January 2009 Accepted 5 April 2009
متن کاملNeural Network Classification Using Error Entropy Minimization
One way of using the entropy criteria in learning systems is to minimize the entropy of the error between two variables: typically, one is the output of the learning system and the other is the target. This framework has been used for regression. In this paper we show how to use the minimization of the entropy of the error for classification. The minimization of the entropy of the error implies...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2010
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.e93.d.2005