Speaker independent speech recognition with robust out-of-vocabulary rejection

With the increased use of speech recognition outside of the lab environment, the need for better out-of-vocabulary (OOV) rejection techniques is critical for the continued success of this user interface. Not only must future speech recognition systems accurately reject OOV utterances, but they must also maintain their performance in mismatched (i.e., noisy) conditions. In this paper we extend our work on low-complexity, high-accuracy speaker independent speech recognition. We present a novel rejection criterion that is shown to be robust in mismatched conditions. This technique continues our emphasis on speech recognition for resource limited applications, by providing a solution that is highly scalable, requiring no additional memory and no significant increase in computation. The technique is based on the use of multiple garbage models (on the order of 100 or more) and a novel ranking method to achieve robust performance. Results on a large, 166-speaker database are provided for different design parameters, and they are compared to traditional threshold methods. Performance is shown to be superior to the approximated optimal Bayes reject rule.