Classifying the socio-situational settings of transcripts of spoken discourses

In this paper, we investigate automatic classification of the socio-situational settings of transcripts of a spoken discourse. Knowledge of the socio-situational setting can be used to search for content recorded in a particular setting or to select context-dependent models for example in speech recognition. The subjective experiment we report on in this paper shows that people correctly classify 68% the sociosituational settings. Based on the cues that participants mentioned in the experiment, we developed two types of automatic socio-situational setting classification methods; a static socio-situational setting classification method using support vector machines (S3C-SVM), and a dynamic socio-situational classification method applying dynamic Bayesian networks (S3C-DBN). Using these two methods, we developed classifiers applying various features and combinations of features. The S3C-SVM method with sentence length, function word ratio, single occurrence word ratio, part of speech (POS) and words as features results in a classification accuracy of almost 90%. Using a bigram S3C-DBN with POS tag and word features results in a dynamic classifier which can obtain nearly 89% classification accuracy. The dynamic classifiers not only can achieve similar results as the static classifiers, but also can track the socio-situational setting while processing a transcript or conversation. On discourses with a static social situational setting, the dynamic classifiers only need the initial 25% of data to achieve a classification accuracy close to the accuracy achieved when all data of a transcript is used. 2013 Elsevier B.V. All rights reserved.