A discriminatively trained Hough Transform for frame-level phoneme recognition

Despite recent advances in the use of Artificial Neural Network (ANN) architectures for automatic speech recognition (ASR), relatively little attention has been given to using feature inputs beyond MFCCs in such systems. In this paper, we propose an alternative to conventional MFCC or filterbank features, using an approach based on the Generalised Hough Transform (GHT). The GHT is a common approach used in the field of image processing for the task of object detection, where the idea is to learn the spatial distribution of a codebook of feature information relative to the location of the target class. During recognition, a simple weighted summation of the codebook activations is commonly used to detect the presence of the target classes. Here we propose to learn the weighting discriminatively in an ANN, where the aim is to optimise the static phone classification error at the output of the network. As such an ANN is common to hybrid ASR architectures, the output activations from the GHT can be considered as a novel feature for ASR. Experimental results on the TIMIT phoneme recognition task demonstrate the state-of-the-art performance of the approach.