Phoneme recognition using Boosted Binary Features

In this paper, we propose a novel parts-based binary-valued feature for ASR. This feature is extracted using boosted ensembles of simple threshold-based classifiers. Each such classifier looks at a specific pair of time-frequency bins located on the spectro-temporal plane. These features termed as Boosted Binary Features (BBF) are integrated into standard HMM-based system by using multilayer perceptron (MLP) and single layer perceptron (SLP). Preliminary studies on TIMIT phoneme recognition task show that BBF yields similar or better performance compared to MFCC (67.8% accuracy for BBF vs. 66.3% accuracy for MFCC) using MLP, while it yields significantly better performance than MFCC (62.8% accuracy for BBF vs. 45.9% for MFCC) using SLP. This demonstrates the potential of the proposed feature for speech recognition.