Continuous speech recognition under non-stationary musical environments based on speech state transition model

We propose a non-stationary noise reduction method based on the speech state transition model. Our proposed method estimates the speech signal under non-stationary noisy environments such as musical background by applying the speech state transition model to Kalman filtering estimation. The speech state transition model represents the state transition of the speech component in non-stationary noisy speech and is modeled by using Taylor expansion. In this model, the state transition of the noise component is estimated by using linear predictive estimation. In order to evaluate the proposed method, we carried out large vocabulary continuous speech recognition experiments under 3 types of music and compared the results with the conventional parallel model combination (PMC) method in word accuracy rate. As a result, the proposed method obtained a word accuracy rate that was superior to PMC