Recently, trajectory HMM has been shown to improve the performance of both speech recognition and speech synthesis. For efficiency, state sequence is required to compute likelihood for trajectory HMM which limits its use to N-best rescoring for speech recognition. Motivated by the success of models with temporally varying parameters, this paper proposes a Temporally Varying Feature Mapping (TVFM) model to transform the feature vector sequence such that the trajectory information as modelled by trajectory HMM is suppressed. Therefore, TVFM can be perceived as an implicit trajectory modelling technique. Two approaches for estimating the TVFM parameters are presented. Experimental results for phone recognition on TIMIT and word recognition on Wall Street Journal show that promising results can be obtained using TVFM.
Bibliographic reference. Sim, Khe Chai / Liu, Shilin (2010): "Semi-parametric trajectory modelling using temporally varying feature mapping for speech recognition", In INTERSPEECH-2010, 2982-2985.