On-line garbage modeling for word and utterance verification in natural numbers recognition

On-line garbage modeling techniques have been shown to be a good alternative to traditional explicitly-trained garbage HMMs due to their low computational cost, their independence respect to any garbage training set and to the good results provided in several tasks related with rejection and keyword spotting. We present an extension of this techniques to word, pulse and utterance verification for real continuous speech recognition based applications. The combination of this method with application-dependent knowledge has provided a drastic control on the false alarm rate (down to 3.2%), while maintaining low and balanced performance in terms of string error rate and string rejection rate.