Robust Speech Recognition Using PCA-Based Noise Classification

This paper proposes a new environmental noise classification using principal component analysis (PCA) 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 PCA to a set of noise features, and results from PCA are used by a pattern classifier for noise classification. Instead of including both clean and noisy environments in a single classifier, two-step classification is introduced by separating the clean from noisy environments and then identifying the type of noisy environments. The proposed model is evaluated with four types of noise: white, pink, babble, and car from NOISEX-92 and shows a promising result regardless of signal-to-noise ratio (SNR).