Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Speaker Independent Phoneme Recognition by MLP Using Wavelet Features

Omar Farooq, Sekharjit Datta

Department of Electronic and Electrical Engineering, Loughborough University, UK

Feature extraction is one of the most important tasks in speech recognition system. Most of the speech recognition systems use Short Time Fourier Transform (STFT) for the derivation of features from the spoken utterances. In this paper we try to exploit the higher time-frequency resolution property of Discrete Wavelet Transform (DWT) for extraction of speaker independent features. The features are extracted every 8ms to account for the faster changes in the phoneme. These features are then used to train a Multi-Layer Perceptron (MLP) classifier for the recognition of phonemes.


Full Paper

Bibliographic reference.  Farooq, Omar / Datta, Sekharjit (2000): "Speaker independent phoneme recognition by MLP using wavelet features", In ICSLP-2000, vol.1, 393-396.