Wavelet Energy based Satistical Learning Approaches to Vocoid Consonant Recognition

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 speech feature extraction techniques, Wavelet Transform (WT) are highly capable of interpreting information content of the signal. This paper focuses on the recognition of Malayalam Vocoid Consonant (VC) speech units, a unique characteristic of the Malayalam language, using WT based Wavelet Energy (WE) parameters to capture the acoustical properties of each speech units. In the classification stage ML based on statistical approaches using with k – Nearest Neighbor (k – NN) is implemented. From the experimental results it is reported that k-NN algorithm can be perform well with wavelet family db5 compared with others in speaker independent environment.