噪声环境下智能机器人语音控制特征提取方法

针对机器人的应用场合通常存在各种噪声干扰的问题,提出了一种基于稀疏编码的语音特征提取方法.利用稀疏编码能稀疏表示语音的特性,在梅尔频域对语音增强后提取特征,将稀疏去噪与语音特征提取相融合,实现了混噪语音的有效补偿.在预设场景中的实验结果表明,与现有特征提取方法相比,所提出的语音特征提取方法能有效降低噪声对语音特征的影响,提高机器人语音控制的性能.

Speech Control Feature Extraction for Intelligent Robotics Under Noisy Environments

Despite of significant progress on speech recognition, current techniques cannot satisfy the demands of real applications in robot controls, the main reason is that various noises in environments of robot control substantially degrade the performance of speech recognition. A feature extraction method is proposed based on sparse coding. This method makes use of the de-noising merit of sparse coding and extracts features after removing noise in Mel-frequency domain. Such a strategy integrates spare coding into speech feature extraction and can reduce the effect of noise. Experiments in speech recognition tasks show that the feature proposed possesses strong robustness against various noises and improves the performance of speech recognition in noisy environments.