Hybrid support vector machine/hidden Markov model approach for continuous speech recognition
A hybrid support vector machine (SVM) and hidden Markov model (HMM) approach is proposed for designing continuous speech recognition systems. Using novel properties of SVMs and combining them with HMMs one can obtain models that map easily to hardware and lead to more modular and scalable design. The overall architecture of the proposed system is based on the MAP (maximum a posteriori) framework which offers a direct, feedforward recognition model. The SVMs generate smooth estimates of local transition probabilities in the HMM, conditioned on the acoustic inputs. The transition probabilities are then used to estimate the global posterior probabilities of HMM state sequences. A parallel architecture that implements a simple speech recognition model in real-time is presented.