通过设计金属构件的敲击检测试验，研究分析了敲击信号的频谱特征，并对利用语音识别技术识别金属材料内部缺陷的可能性进行了验证。结果表明，缺陷的存在会导致敲击信号的频谱向低频段移动或频率主峰发生分裂，利用敲击信号的MFCC（梅尔频率倒谱系数）特征参数构建的GMM-HMM （高斯混合-隐马尔可夫模型）可有效识别出不同类别的缺陷试件，但识别结果易受到噪声影响；经“二元信息融合+噪声泛化”算法改进后的GMM-HMM在强烈噪声干扰下（10 dB 信噪比）仍具有较高的识别率，且在敲击声信号融合权重为0.6时识别率达到最优（99.3%）。

In this paper, an experiment of percussion detection was designed and feature extraction methods of percussion signals were studied, and then the possibility of using speech recognition technology to recognize internal defects in metal materials was verified. The results show that defects appeared to cause the frequency spectrum of the percussion signals to move to low frequency band or to be split in more peaks.GMM-HMM (Gaussian mixture-hidden Markov model) built on MFCC (Mel-scale frequency cepstral coefficients) parameters of percussion signals could identify different types of specimens effectively but were liable to be affected by noise. The model improved with “binary information fusion+noise generalization” has a higher recognition rate in the extreme noise circumstance. Under the noise (10 dB SNR) circumstance, the recognition rate of the improved GMM-HMM reached the optimal value 99.3% with the sound information fusion rate at 0.6.