Investigations on features for log-linear acoustic models in continuous speech recognition

Hidden Markov Models with Gaussian Mixture Models as emission probabilities (GHMMs) are the underlying structure of all state-of-the-art speech recognition systems. Using Gaussian mixture distributions follows the generative approach where the class-conditional probability is modeled, although for classification only the posterior probability is needed. Though being very successful in related tasks like Natural Language Processing (NLP), in speech recognition direct modeling of posterior probabilities with log-linear models has rarely been used and has not been applied successfully to continuous speech recognition. In this paper we report competitive results for a speech recognizer with a log-linear acoustic model on the Wall Street Journal corpus, a Large Vocabulary Continuous Speech Recognition (LVCSR) task. We trained this model from scratch, i.e. without relying on an existing GHMM system. Previously the use of data dependent sparse features for log-linear models has been proposed. We compare them with polynomial features and show that the combination of polynomial and data dependent sparse features leads to better results.