Abstract
The quality feature set is a key of importance of successful speech recognition system. The quality of features is estimated by classification error. Yet, this method is limited as the classification experiments must be run with each feature system. The major issue of this paper is to propose the method for quality estimation of speech recognition features that is based on metrics and does not require classification experiments. Experimental researches were made in context of Nearest neighbour classifier usage. Within the proposed method PLP was established to have the higher quality comparing to LFCC. The adequateness of the method was validated by Nearest neighbour classification error. Ill. 3, bibl. 25, tabl. 1 (in English; abstracts in English and Lithuanian). DOI: http://dx.doi.org/10.5755/j01.eee.118.2.1165
Highlights
The selection of the quality feature system is the key of successful speech recognition system
The major issue of current research is to propose the method for quality estimation of speech recognition feature system with the approach that doesn‘t require performing classification experiments
The paper attributes to the issue of quality estimation method of speech recognition features
Summary
The selection of the quality feature system is the key of successful speech recognition system. The concept of quality can be defined by comparing a set of inherent characteristics with a set of requirements If these subjects are met, high quality is achieved [16]. On the contrary high classification error is achieved for not quality feature system. Currently quality of features is used to estimate by calculating the classification error. This method is limited as it causes running classification experiments with each explored feature system. The major issue of current research is to propose the method for quality estimation of speech recognition feature system with the approach that doesn‘t require performing classification experiments. The proposed method for quality estimation of speech recognition feature system is presented. Metrics for quality estimation of speech recognition features are displayed.
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