关键词: 农产品市场信息；语音识别；最小均方误差；特征补偿

Abstract:

At present, many portable devices of information collection were designed and applied to agricultural product market information acquisition. Speech recognition has made great progress, and voice interface can be applied to these devices, but the general robust recognition algorithms are too complicated. In order to solve this problem, this paper proposed a robust speech recognition method, suitable for the devices operation environment. At first, MMSE amplitude estimator was used to enhance the noisy speech signal and improve signal-to-noise ratio (SNR) of the input signal; and then, for the speech distortion and residual noise after enhancement, the cepstrum mean and variance normalization (CMVN) methods were used to compensate. The experimental results showed that the combined algorithm could effectively improve the system correct recognition rate, especially under low SNR (0~10 dB) environment.

Key words: agricultural market information, speech recognition, minimum mean square error, feature compensation