Robust and efficient confidence measure for isolated command recognition
A new confidence measure for isolated command recognition is presented. It is versatile and efficient in two ways. First, it is based exclusively on the speech recognizer's output. In addition, it is robust to changes in the vocabulary, acoustic model and parameter settings. Its calculation is very simple and it is based on the computation of a pseudo-filler score from an N-best list. Performance is tested in two different command recognition applications. Finally, it is efficient to separate correct results both from incorrect ones and from false alarms caused by out-of-vocabulary elements and noise.
In this article we study monotone stochastic orders for real-valued processes with independent increments and derive sufficient deterministic conditions which are easy to verify and suitable for applications. Our proof is based on an explicit construction of a coupling. We also derive comparison results for L\'evy processes and semimartingales with conditionally independent increments.