Analysis of HMM Temporal Evolution for Automatic Speech Recognition and Verification

Abstract

This paper proposes a double layer speech recognition and utterance verification system based on the analysis of the temporal evolution of HMM’s state scores. For the lower layer, it uses standard HMM-based acoustic modeling, followed by a Viterbi grammar-free decoding step which provides us with the state scores of the acoustic models. In the second layer, these state scores are added to the regular set of acoustic parameters, building a new set of expanded HMMs. This new paremeter models the acoustic HMM’s temporal evolution. Using the expanded set of HMMs for speech recognition a significant improvement in performance is achieved. Next, we will use this new architecture for utterance verification in a ”second opinion” framework. We will consign to the second layer evaluating the reliability of decoding using the acoustic models from the first layer. An outstanding improvement in performance versus a baseline verification system has been achieved with this new approach.