A new approach for modeling OOV words

This paper addressed the problem of Out-Of-Vocabulary (OOV) utterance detection in small vocabulary telephone keyword spotting system. We propose a new approach for modeling OOV words in the scenario of a small vocabulary of telephone keyword spotting system. The paper adopt the semi-continuous Hidden Markov Model with multiple codebooks to modeling the keywords. We propose a two pass procedure to spot the real keyword occurrence. In the first pass, the normal viterbi search procedure is applied, with the appropriate defined and trained garbage models and silence models. The output of this stage produces the N-best word hypothesis The second pass, which can be seen as a verification procedure, take the first pass output as focuses. This approach is mainly constructing a “dynamic anti-model” based on the detected hypothesis keyword model and the current input acoustic information.