Incorporating linguistic knowledge and automatic baseform generation in acoustic subword unit based speech recognition

A major challenge in speech recognition based on acoustic subword units is creating a lexicon which is robust to interand intra-speaker variations. In this paper we present two di erent approaches for incorporating simple word-level linguistic knowledge into the labelling step of the training procedure. The proposed systems also utilise a scheme for combined optimisation of baseforms and subword models. For the TI46 database, these methods are shown to greatly improve the performance compared to an acoustic subword based speech recogniser employing unsupervised labelling, and they are found to perform as well as systems utilising whole-word models and context independent phoneme models.