Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. This work includes two speech recognition methods. First one is a hybrid approach with Discrete Wavelet Transforms and Artificial Neural Networks and the second method uses a combination of Wavelet Packet Decomposition and Artificial Neural Networks. Features are extracted by using Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results obtained show the efficiency of these techniques in recognizing speech.

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations.

Speech recognition is a fascinating application of Digital Signal Processing and has many real-world applications. In this paper, a speech recognition system is developed for isolated spoken words using Discrete Wavelet Transforms (DWT) and Artificial Neural Networks (ANN). Speech signals are one-dimensional and are random in nature. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Daubechies wavelets are employed here. A multilayer neural network trained with back propagation training algorithm is used for classification purpose. The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results show good recognition accuracy and the efficiency of combining these two techniques.

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more naturally and efficiently. In this work, a speech recognition system is developed for recognizing digits in Malayalam. For recognizing speech, features are to be extracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is performed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Discrete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hybrid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.

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