Through this training procedure, the algorithms learn how the sociometrics depend on the low-level speech metrics, and consequently, are able to compute the sociometrics from speech recordings in an automated fashion, without further help of experts. Next, the resulting annotated dialogs are used to train the machine learning algorithms in a supervised manner. Several experts assessed the sociometrics for each of those dialogs. To this end, a corpus of 150 brief two-person dialogs in English was collected. In the proposed approach, machine learning algorithms are designed to compute the sociometrics (level of interest, agreement, and dominance) in real-time from combinations of low-level speech metrics.

Such quantitative measures can be used to provide real-time feedback to the speakers, for instance, to alarm to speaker when the voice is too strong (speaking mannerism), or when the conversation is not proceeding well due to disagreements or numerous interruptions (social behavior). The social behavior is quantified by sociometrics including level of interest, agreement, and dominance. Speaking mannerisms are quantitatively assessed by low-level speech metrics such as volume, rate, and pitch of speech. A real-time system is proposed to quantitatively assess speaking mannerisms and social behavior from audio recordings of two-person dialogs.

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