No abstract

Classifiers in Automatic Speech Recognition (ASR) aims to improve the generalization ability of the machine learning and improve the recognition accuracy in noisy environments. This paper discusses the classification performance of Hidden Markov Models (HMM) and Support Vector Machines (SVM) applied to a wavelet front end based ASR. The experiments are performed on speaker independent TIMIT database which are trained in a clean environment and later tested in the presence of Additive White Gaussian Noise (AWGN) for various SNR levels using the HTK toolkit and SVM Light software tool. Experiments indicate that for large vocabulary the wavelet front end and the Multiclass SVM classifier with RBF kernel performs better than the conventional HMM classifier. Keywords-hidden markov models; support vector machines; automatic speech recognition; perceptual wavelet packetsI.

Noisy ambient conditions pose a challenge to speech recognition, increasing the acoustic confusability, thereby looking for powerful acoustic models to improve the generalization ability of the machine learning and improve the recognition accuracy. This paper discusses a hybrid classifier that harness the power of hidden markov models (HMM) and the discriminative support vector machines (SVM) applied to a wavelet front end based automatic speech recognition (ASR) system. The experiments are performed on speaker independent TIMIT database which are trained in a clean environment and later tested in the presence of additive white gaussian noise (AWGN) for various SNR levels using the HTK toolkit, SVMLib and SVMLight software tool. Experiments indicate that for large vocabulary the classification power of SVMs and the elegant iterative training algorithms for the estimation of HMMs together as a hybrid classifier with the wavelet front end performs better than the conventional classifiers.