Handwritten word spotting (HWS) is traditionally performed as an image matching task between one or multiple query images and a set of word images in a document. In this article, we address the word spotting problem as a hidden Markov model (HMM) word verification problem and demonstrate the importance of score normalization for improving detection performance. Our main contribution is the introduction of a novel score normalization technique in which the conventional HMM filler model is simplified by using a Gaussian mixture model (GMM). The accuracy of the proposed score normalization is on par with the traditional HMM-based score normalization approaches but it has a lower computational cost. We also identify an interesting special case, the semi-continuous HMM, where the proposed score normalization formalism fits very elegantly and comes at a negligible cost.