Author(s): SONIA SUNNY, DAVID PETER S, POULOSE JACOB K.
Journal: International Journal of Machine Intelligence
ISSN 0975-2927
Volume: 3;
Issue: 4;
Start page: 318;
Date: 2011;
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Keywords: Speech Recognition | Digits database | Feature Extraction | Wavelet Packet Decomposition | Classification | Artificial Neural Networks.
ABSTRACT
This paper introduces an efficient method for recognizing spoken digits using a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks (ANN) classifier. Speech recognition is a fascinating application of Digital Signal Processing. There has been lot of research in the area of speech recognition for different languages. Digits in Malayalam, which belong to one of the four Dravidian languages of Southern India, are used to create the database. Wavelet Packet Decomposition is used for feature extraction in the time-frequency domain. Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). Due to the multi-resolution characteristics and efficient time frequency localizations, wavelets are very much suitable for processing non stationary signals like speech. ANNs are utilized in this work due to their parallel distributed processing, distributed memories, error stability, and pattern learning distinguishing ability. The experimental results show the effectiveness of this hybrid architecture in recognizing speech.
Journal: International Journal of Machine Intelligence
ISSN 0975-2927
Volume: 3;
Issue: 4;
Start page: 318;
Date: 2011;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Speech Recognition | Digits database | Feature Extraction | Wavelet Packet Decomposition | Classification | Artificial Neural Networks.
ABSTRACT
This paper introduces an efficient method for recognizing spoken digits using a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks (ANN) classifier. Speech recognition is a fascinating application of Digital Signal Processing. There has been lot of research in the area of speech recognition for different languages. Digits in Malayalam, which belong to one of the four Dravidian languages of Southern India, are used to create the database. Wavelet Packet Decomposition is used for feature extraction in the time-frequency domain. Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). Due to the multi-resolution characteristics and efficient time frequency localizations, wavelets are very much suitable for processing non stationary signals like speech. ANNs are utilized in this work due to their parallel distributed processing, distributed memories, error stability, and pattern learning distinguishing ability. The experimental results show the effectiveness of this hybrid architecture in recognizing speech.
