Options for Modelling Temporal Statistical Dependencies in an Acoustic Model for ASR

Traditionally, ASR systems are based on hidden Markov models with Gaussian mixtures modelling the state-conditioned feature distribution. The inherent assumption of conditional independence, stating that a feature's likelihood solely depends on the current HMM state, makes the search computationally tractable, nevertheless has also been identified to be a major reason for the lack of robustness of such systems. Linear dynamic models have been proposed to overcome this weakness by employing a hidden dynamic state process underlying the observed features. Though performance of linear dynamic models on continuous speech/phone recognition tasks has been shown to be superior to that of equivalent static models, this approach still cannot compete with the established acoustic models. In this paper we consider the combination of hidden Markov models based on Gaussian mixture densities (GMM-HMMs) and linear dynamic models (LDMs) as the acoustic model for automatic speech recognition systems. In doing so, the individual strengths of both models, i.e. the modelling of long-term temporal dependencies by the GMM-HMM and the direct modelling of statistical dependencies between consecutive feature vectors by the LDM, are exploited. Phone classification experiments conducted on the TIMIT database indicate the prospective use of this approach for the application to continuous speech recognition.