Showing papers on "Support vector machine published in 1970"

Robust Speech Recognition Using KPCA-Based Noise Classification

Abstract: This paper proposes an environmental noise classification method using kernel principal component analysis (KPCA) for robust speech recognition. Once the type of noise is identified, speech recognition performance can be enhanced by selecting the identified noise specific acoustic model. The proposed model applies KPCA to a set of noise features such as normalized logarithmic spectrums (NLS), and results from KPCA are used by a support vector machines (SVM) classifier for noise classification. The proposed model is evaluated with 2 groups of environments. The first group contains a clean environment and 9 types of noisy environments that have been trained in the system. Another group contains other 6 types of noises not trained in the system. Noisy speech is prepared by adding noise signals from JEIDA and NOISEX-92 to the clean speech taken from NECTEC-ATR Thai speech corpus. The proposed model shows a promising result when evaluating on the task of phoneme based 640 Thai isolatedword recognition.