Using spatial correlation information in speech recognition

Acoustic model training is very important in speech recognition. But in traditional training algorithm, we take each state separately, and the relationship between different states is not considered. In this paper we bring forward a novel idea of using the correlation information between states, which is called “spatial correlation”. We describe this correlation information as linear constraints. According to phonetic knowledge, we firstly divide states into small groups named “correlation sub-space”. In every sub-space, we use eigen value decomposition to get linear constraints. The constraints are then used in a new training algorithm. Experiments of the new training algorithm show significant improvement over traditional training algorithm.