Speech recognition in the articulatory domain: investigating an alternative to acoustic HMMs
The hidden Markov model (HMM) has proven to be the model which made large-vocabulary automatic speech recognition (ASR) possible. The HMM is robust, versatile and has at its disposal a host of efficient algorithms to deal with training, speaker adaptation and recognition. However there is nothing uniquely speech orientated about the HMM. In fact, certain assumptions are made of speech which are known to be untrue. For example, speech is modelled as a piecewise stationary process when we know it to be continuous. Also co-articulation, which should be a rich source of information, simply provides unwanted variation. This variation is generally taken into account by modelling every phone in every context which in turn leads to problems of data sparcity, making elaborate parameter tying schemes necessary.