语音识别技术在咳嗽声音自动识别中的应用

目的 利用双门限法、Mel频率倒普系数(MFCC)法及矢量量化(VQ)法的语音识别技术对咳嗽声音进行自动识别.方法 在安静环境下,对5例健康成年人和15例咳嗽患者的非咳嗽和咳嗽声音进行录音,分别随机分为训练样本和测试样本.训练样本用于生成咳嗽识别软件的码本,并用该码本对测试样本进行自动识别分析.同时与人工识别的结果进行对比,计算敏感性、特异性,记录两种方法的识别时间.结果 用于码本生成的咳嗽声音和非咳嗽声音均为200次,测试样本的咳嗽和非咳嗽声音分别为375次和125次.人工识别和通过码本自动识别测试样本的时间分别为33 min 18 s和1min 35 s;码本自动识别咳嗽声音的敏感性和特异性分别为98.93%和100%.结论 基于VQ的双门限法及MFCC法可用于咳嗽声音的自动识别.

Application of speech recognition technology in the automatic recognition of cough sounds

Objective To investigate the feasibility of speech recognition technologies including double threshold method, Mel frequency cepstral coefficient (MFCC) and vector quantization (VQ) in automatic recognition of cough sounds. Methods Five healthy adults and 15 patients with cough were recruited for recording of both non- cough and cough sounds in a quiet environment. The records were randomized into training and testing samples. The training samples were used to generate the code book for cough recognition software, which was then used to recognize and analyze the testing samples automatically.The sensitivity and specificity of recognition were calculated by comparison with outcomes from human ear recognition. The recognition time in two approaches was recorded. Results Two hundred cough and 200 noncough sound samples were used to generate the code book for cough recognition software, while 375 cough sound samples and 125 non-cough sound samples were used as the testing samples. The recognition time of the testing samples needed was 33 minutes and 18 seconds by human ear recognition vs 1 minute and 35seconds by code book-based automatic recognition. In addition, the sensitivity and specificity in code bookbased automatic recognition of the cough sound were 98.93% and 100% respectively. Conclusion The double threshold method based on VQ and MFCC appears feasible in automatic recognition of cough sounds.