Model adaptation of factorial HMMS for multipitch tracking

Factorial hidden Markov models (FHMMs) are used for tracking the pitch of two interacting speakers [1]. In this statistical approach, the characteristics of each speaker are captured by pre-trained models. Speaker models that match the test conditions well allow for high tracking performance, however the availability of such models is unrealistic. To extend the applicabiliy of the FHMM framework, we develop an EM-like iterative adaptation algorithm which is capable to adapt the model parameters to the specific situation, e.g. acoustic channel, using only speech mixture data. Model adaptation is empirically evaluated using real room recordings of mixture utterances from the GRID corpus.