Analysis of breathy, emergency and pathological stress classes

Abstract

Recently, man–machine interaction based on speech recognition has taken an increasing interest in the field of speech processing. The need for machine to understand the human stress levels in a speaker-independent manner, to prioritize the situation, has grown rapidly. A number of databases have been used for stressed speech recognition. Majority of the databases contain styled emotions and Lombard speech. No studies have been reported on stressed speech considering other stress conditions like emergency, breathy, workload, sleep deprivation and pathological condition. In this work, a new stressed speech database is recorded by considering emergency, breathy and pathological conditions. The database is validated with statistical analysis using two features, mel-frequency cepstral coefficient (MFCC) and Fourier parameter (FP). The results show that these recorded stress classes are effectively characterized by the features. A fivefold cross-validation is carried out to assess how the statistical analysis results are independent of the dataset. Support vector machine (SVM) is used to classify different stress classes.