Mining speech: automatic selection of heterogeneous features using boosting

We investigate feature selection applied to automatic speech recognition (ASR) systems. We focus on systems based on support vector machines (SVM), which can naturally use features optimized for each classifier. We present a new method for feature selection based on the AdaBoost algorithm. This method was an order of magnitude faster than a similar one, while leading to equivalent accuracy. Experiments with phone classification using TIMIT and a total of 760 features (PLP, MFCC, Seneff´s, formants, etc.) indicated that the proposed method automatically discovered important information in the data. When using only 25 selected features per SVM, the accuracy was higher than when using a homogeneous set of 118 features based on PLP (perceptual linear prediction) coefficients.