Robust speech recognition techniques using a radial basis function neural network for mobile applications

In this paper we present the development of robust speech recognition techniques for voice activated dialing (VAD) for cellular phone in a car. Noise seduction techniques are implemented before modeling the speech parameters in the homomorphic domain. The FFT derived cepstral coefficients are liftered and then Mel-scale warped to generate the feature vector. Radial Basis Function Neural Network is used as final classifier and variation in its structure is studied to evaluate its real-time performance, Results on speaker-dependent and speaker-independent recognition across -5 dB to 25 dB SNR range are presented and a performance evaluation is carried out for different front-end techniques. The system is evaluated using NOISEX-92 database.