Automatic recognition of isolated spoken digits is one of the most challenging tasks in the area of Automatic Speech Recognition. In this paper, Database Development and Automatic Speech Recognition of Isolated Pashto Spoken Digits from Sefer (0) to Naha (9) has been presented. A number of 50 individual Pashto native speakers (25 male and 25 female) of different ages, ranging from 18 to 60 years, were involved to utter from Sefer (0) to Naha (9) digits separately. Sony PCM-M 10 linear recorder is used for recoding purpose in the office and home in noise free environment. Adobe audition version 1.0 is used to split the audio of digits into individual digits and result is saved in .wav format. Mel frequency cepstral coefficients is used to extract speech features. K nearest neighbor classifier is used for the first time up to author knowledge in Pashto language to classify the features of speech and compare its accuracy with linear discriminate analysis. The experimental results are evaluated, and the overall average recognition exactitude of 76.8 % is obtained.

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This paper introduces an accurate time-domain approach to model and classify the Malayalam consonantVowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy.

This paper presents a new approach to model face images using the state space feature parameters. We also present a novel feature extraction methodfor the recognition offace images based on their grayscale images eliminating any step of preprocessing. Experiments are performed using the standard AT & T (formerly, ORL face database) face database containing 400 face images of 40 different individuals.The state space map and state space point distribution graph drawn for 400 individuals 'face image shows the credibility ofthe method. To show the nonlinear nature of the face images the fractal dimension is also computed from the state space map of the each face image using the box count method In the recognition stage we used k-NN classifier, and the proposed SSPD feature is found to be promising, and this is the first attempt ofthis kind in the field offace recognition.

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