Fast and robust stochastic segment model for Mandarin digital string recognition

Based on the analysis and comparisons of complexity between stochastic segment model (SSM) and hidden Markov model (HMM) in this paper, we presented a fast and robust SSM, which yields a 94.75% speaker-independent performance on Mandarin digit string recognition. This result is better than HMM based system at the same level of computational complexity and just only a little slower than HMM in the running time. We also studied a region based discriminative method, which achieves 18.0% error rate reduction for substitution error and 95.08% accuracy for Mandarin digit string recognition.