Keyword spotting with duration constrained HMMs

This paper focuses on a near optimal detector based on dynamic programming for voice activation. The algorithm is analysed and evaluated against the optimal detector while taking both accuracy and complexity factors into consideration. It is shown that when using the more restrictive framework of Hidden Markov models (HMMs) with state duration constraints this is improving both the accuracy of the approximate detector as well as providing a substantial boost to the detection performance.