This paper describes the implementation of two isolated digit recognition techniques and is a comparison between the algorithms implemented. Any digit recognition comprises of mainly two stages feature extraction and similarity evaluation. Here, two feature extraction techniques, namely linear predictive cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are implemented...

In the present work we overview some recently proposed discrete Fourier transform (DFT)and discrete wavelet packet transform (DWPT)-based speech parameterization methods and evaluate their performance on the speech recognition task. Specifically, in order to assess the practical value of these less studied speech parameterization methods, we evaluate them in a common experimental setup and comp...

In HMM-based speech synthesis, trained statistical models (context-dependent HMMs) are used to predict duration and generate parameters like mel-cepstral coefficients, log F0 values, and bandpass voicing strengths using the maximum likelihood parameter generation algorithm including global variance (Toda et al, 2007). In the later stages, F0 parameters, bandpass voicing strengths, and the five ...

Sung language recognition relies on both effective feature extraction and acoustic modeling. In this paper, we study rhythm based music segmentation in which the frame size varies in proportion to inter-beat interval of the music, in contrast to fixed length segmentation (FIX) in spoken language recognition. We show that acoustic feature extracted from the BSS scheme outperforms that from FIX. ...