An algorithm for decoding commands spoken in an intelligent environment through iterative vocabulary reduction is presented. Current research in the field of speech recognition focuses primarily on the optimization of algorithms for single pass decoding using large vocabularies. While this is ideal for processing conversational speech, alternative methods should be explored for different domains of speech, specifically commands issued verbally in an intelligent environment. Such commands have both an explicitly defined structure and a vocabulary limited to valid task descriptions. We propose that a multiple pass context-driven decoding scheme utilizing dictionary pruning yields improved accuracy; this occurs when one deals with command structure and a reduced vocabulary. Each iteration incorporates the hypothesis of the previous into its decoding scheme by removing unlikely words from the current language model. We have applied this decoding method to a comprehensive set of spoken commands through the use of Sphinx-4, an Automatic Speech Recognition (ASR) engine using the Hidden Markov Model (HMM). When decoding via HMM, multiple previous states are used to determine the current state, thus utilizing context to aid in intelligent recognition. Our results show that within a fixed domain, multiple pass decoding yields recognition accuracy. Further research must be conducted to optimize practical context driven decoding and to apply the method to larger domains, primarily those of intelligent environments.
展开▼
