Modelling the dynamics of acoustic gaps between speakers during business-to-business sales calls
by Anat Lerner; Vered Silber-Varod; Nehoray Carmi; Yonathan Guttel; Omri Allouche
International Journal of Big Data Intelligence (IJBDI), Vol. 7, No. 4, 2020

Abstract: The value of conversation intelligence in deepening the insights of authentic conversations is a common ground nowadays between researchers and the business community. The rapid development of big data algorithms and technology enables massive amounts of data and meta-data processing, including content, vocal features and body gestures. This study is based on 358 business-to-business (B2B) sales calls at the discovery stage. We propose a model to capture the dynamics of acoustic gaps between the sales representatives and customers by relying solely on the acoustic signal. We extract basic features from the acoustic signal: speech proportion, fundamental frequency (F0), intensity, harmonics-to-noise ratio (HNR), jitter and shimmer. We focus on the differences between the four speakers' role-gender groups (e.g., female-representative with female-customer). We found significant differences in the behavioural patterns of the dynamics between these four groups. The study demonstrates that using delta metrics to assess the interactions leads to new insights.

Online publication date: Wed, 31-Mar-2021

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