A Paradigm for Limited Vocabulary Speech Recognition Based on Redundant Spectro-Temporal Feature Sets

摘要

Speech recognition techniques have come to rely almost completely on HMM based frameworks. In this paper, we present a novel paradigm for small-vocabulary speech recognition based on a recently proposed word spotting technique. Recent work using discriminative classifiers with ordered spectro-temporal features to detect the presence of keywords obtained encouraging improvements over HMM-based models. We propose to extend this approach to recognize continuous speech in our work. Our method uses discriminative models to predict which words are present in a speech signal and hypothesize their locations. A graph search using dynamic programming is then used to obtain the most likely sequence of words from the hypothesis set produced as a result of combining the results from the discriminative word classifiers. While this approach doesn't perform as well as state-of-the-art ASR systems, it can be particularly useful for languages with small amounts of annotated data available.