CONF Roy_ICASSP11_2011/IDIAP Phoneme Recognition using Boosted Binary Features Roy, Anindya Magimai-Doss, Mathew Marcel, Sébastien EXTERNAL http://publications.idiap.ch/attachments/papers/2011/Roy_ICASSP11_2011.pdf PUBLIC IEEE Intl. Conference on Acoustics, Speech and Signal Processing 2011 2011 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.