Sarika Hegde

  • Citations Per Year
Learn More
Multiple Classifier System (MCS) is designed by combining two or more classifiers. MCS helps in increasing the accuracy of classification compared to the performance of the individual classifier. In this paper, Multiple Classifier System is implemented for automatic speech recognition. The combined classifier takes the final decision on predicted class(More)
Automatic speech recognition (ASR) for a given audio file is a challenging task due to the variations in the type of speech input.Variationsmay be the environment, language spoken, emotions of the speaker, age/gender of speaker etc. The two main steps in ASR are converting the audio file into features and classifying it appropriately. Basic unit of speech(More)
Automatic Speech Recognition (ASR) involves mainly two steps; feature extraction and classification (pattern recognition). Mel Frequency Cepstral Coefficient (MFCC) is used as one of the prominent feature extraction techniques in ASR. Usually, the set of all 12 MFCC coefficients is used as the feature vector in the classification step. But the question is(More)
Feature extraction technique plays an important role in Automatic speech recognition. There are many techniques for extracting the speech features. Capability of various speech features for categorizing speech sounds differs and can be evaluated after applying classification or clustering model. As an alternative approach, a preliminary study (before(More)
Music Information Retrieval (MIR) focuses on retrieving useful information from collection of music. The objective of research work in this paper is to explore clustering approaches which can be useful in automatically mining the content from Carnatic instrumental music. The content to be retrieved is the instrument that is primarily used to play the song.(More)
  • 1