A novel and robust parameter training approach for HMMs under noisy and partial access to states

作者：Ozkan Huseyin^*; Akman Arda; Kozat Suleyman S

来源：Signal Processing, 2014, 94: 490-497.

DOI：10.1016/j.sigpro.2013.07.015

• HMM
• ML estimator
• Incomplete data
• Partially observed states

This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as %26quot;partial labeling%26quot; of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the %26quot;achievable margin%26quot; defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions.

• 出版日期2014-1