Boosting localized binary features for speech recognition

In a recent work, the framework of Boosted Binary Features (BBF) was proposed for ASR. In this framework, a small set of localized binary-valued features are selected using the Discrete Adaboost algorithm. These features are then integrated into a standard HMM-based system using either single layer perceptrons (SLP) or multilayer perceptrons (MLP). The features were found to perform significantly better (when coupled with SLP) and equally well (when coupled with MLP) compared to MFCC features on the TIMIT phoneme recognition task. The current work presents an overview of the idea and extends it in two directions: 1) fusion of BBF with MFCC and an analysis of their complementarity, 2) scalability of the proposed features from phoneme recognition to the continuous speech recognition task and reusability on unseen data.