Paying Lip Service?

Abstract

Literature has mainly analyzed the technical attributes of voice for enterprise technical applications, such as voice signature and speech recognition. This paper aims at identifying voice and speech attributes that reflect sentiment and affect (positively or negatively) customer satisfaction levels in voice interactions. The paper used method triangulation that utilizes multiple data sources to gain comprehensive understanding of the domain, including auditory observations, focus group interviews, customer survey, and a review of recent academic studies. The findings indicate that customer experience and satisfaction are influenced by the relative values of voice pitch between the CSR (customer service representative) and the customer. The major conclusion of the study suggests that voice fundamental pitch, speech rate, voice amplitude, and other communication parameters might deploy hidden power and affect service interaction's results. The study opens a new venue for research on social interaction adjustment from the CSR's perspective and from the customer's standpoint.

A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media

“…The consumer textual sentimental opinions related to products require natural language processing (NLP) techniques to identify key phrases and terms that are associated with particular features [5], whereas the visual text in the shape of an image needs optical character recognition (OCR) followed by NLP [6]. The vocal feedback and reviews require voice recognition based on 1D CNN models and handcrafted models [7].…”