In Automatic Speech Recognition (ASR), the presence of Out Of Vocabulary (OOV) words or sounds, within the speech signal, can have a detrimental effect on recognition performance. One common method of solving this problem is to use filler models to absorb the unwanted OOV utterances. A balance between accepting In Vocabulary (IV) words and rejecting OOV words can be achieved by manipulating the values of Word Insertion Penalty and Filler Insertion Penalty. This paper investigates the ability of three different classes of HMM filler models, K-Means, Mean and Baum-Welch, to discriminate between IV and OOV words. The results show that using the Baum-Welch trained HMMs 97.0% accuracy is possible for keyword IV acceptance and OOV rejection. The K-Means filler models provide the highest IV acceptance score of 97.3% but lower overall accuracy. However, the computational complexity of the K-Means algorithm is significantly lower and requires no additional speech training data.
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