T. M. Thasleema

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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(More)
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(More)
This paper reports a study on static pattern classification technique using Support Vector Machine based Decision Directed Acyclic Graph (DDAG) algorithm for the classification of Malayalam Consonant – Vowel (CV) speech unit utterances. Wavelet Transform (WT) based Normalized Wavelet Hybrid Features (NWHF) by combining both Classical Wavelet Decomposition(More)
This paper presents a statistical learning algorithm based on Support Vector Machines (SVMs) for the classification of Malayalam Consonant – Vowel (CV) speech unit in noisy environments. We extend SVM for multiclass classification using Decision Directed Acyclic Graph Support Vector Machine (DDAGSVM) algorithm. For classification, acoustical features are(More)
The performance of the Two Stage FxNLMS algorithm based Active Noise Control (ANC) system is evaluated in the environment of market noise. ANC is normally used for noise cancellation. While cancelling noise, ANC system cancels the noise lies in its entire frequency spectrum. However, an ANC system designed to cancel low frequency (≤500 Hz) noise, can(More)
State – of – the – art Automatic Speech Recognition (ASR) employs rigorous experimental evaluations on large, standard corpora from the real world. In recent years ASR and Machine Learning (ML) algorithms have had a great deal of influences on each other and feature selections can be considered as an essential task in ML. Compared with traditional basic(More)
Recognition of Malayalam CV speech unit is an important research area which has variety of applications in designing human computer interface systems. In the present work we employ LPC parameters for ANN and k-NN based pattern recognition approach for Malayalam CV speech unit recognition .The classification is performed for 36 Malayalam CV units having 3456(More)
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