Speech recognition under non-stationary noisy environments using signal estimation method based on speech state transition model

摘要

In this paper, we propose a non-stationary noise reduction method based on speech state transition model. Our proposed method estimates the speech signal under non-stationary noisy environments such as musical background by applying speech state transition model to Kalman filtering estimation. The speech state transition model represents the state transition of speech component in non-stationary noisy speech and is modeled by using Taylor expansion. In this model, the state transition of 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 musics and compared the results with conventionally used Parallel Model Combination (PMC) method in word accuracy rate. As a result, the proposed method obtained word accuracy rate superior to PMC.