Structured Log Linear Models for Noise Robust Speech Recognition

Abstract

The use of discriminative models for structured classification tasks, such as speech recognition is becoming increasingly popular. This letter examines the use of structured log-linear models for noise robust speech recognition. An important aspect of log-linear models is the form of the features. By using generative models to derive the features, state-of-the-art model-based compensation schemes can be used to make the system robust to noise. Previous work in this area is extended in two important directions. First, a large margin training of sentence-level log linear models is proposed for automatic speech recognition (ASR). This form of model is shown to be similar to the recently proposed structured Support Vector Machines (SVM). Second, based on the designed joint features, efficient lattice-based training and decoding are performed. This novel model combines generative kernels, discriminative models, efficient lattice-based large margin training and model-based noise compensation. It is evaluated on a noise corrupted continuous digit task: AURORA 2.0.