Revolutionizing Call Centers through ASR and Advance Speech Analytics

Authors

  • Ashish Bansal USA Author

DOI:

https://doi.org/10.47363/JAICC/2022(1)E178

Keywords:

Speech to Text, NLP, Call Analytics, Classification, Call Center Technology, ASR

Abstract

Technological improvement and innovation have led to the development of the classic contact call center into an omnichannel call center. The progress has occurred because customers are exposed to the use of SMS but also social media, chats, and other websites. Therefore, customers can contact a company through diverse ways creating a crucial analytics case for most call center companies. 


In this paper, we will briefly outline speech recognition and analytics and discuss their benefits. As well as we will talk about how a hybrid system can help not just converting audio to text but also how downstream NLP tasks can be utilized to get more advance call analytics. These NLP components analyze the transcribed text, extracting the customer’s intent, context, and specific requests. They comprehend the customer’s query and gather the necessary information to generate relevant and accurate responses. Subsequently, a text-to-speech model is employed to convert the generated response back into voice format for delivery to the customer.

Author Biography

  • Ashish Bansal, USA

    Ashish Bansal, USA. 

Downloads

Published

2022-01-29

Issue

Section

Vol 1, Issue 1

How to Cite

Revolutionizing Call Centers through ASR and Advance Speech Analytics. (2022). Journal of Artificial Intelligence & Cloud Computing, 1(1), 1-4. https://doi.org/10.47363/JAICC/2022(1)E178

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