On recognition of spoken Bengali numerals

This paper presents a method for recognizing isolated spoken Bengali numerals. Noisy audio samples have been considered as input in this study. Mel frequency cepstral coefficients (MFCC) have been used for extraction of feature from the audio samples. Vector quantization is applied to reduce the dimension of the feature vectors and to generate a vector codebook for the numerals. The classification is based on the dynamic time warping (DTW) and a minimum distance classifier based on Euclidean distance measure. Both the speaker dependent and speaker independent situations have been considered for checking accuracy. Results show the limitations of MFCC based standard speech processing approach in speaker independent spoken digit recognition scenario in the presence of noise.