Boost Call Center Operations: Google's Speech-to-Text AI Integration
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© 2024 by IJCTT Journal
Volume-72 Issue-7
Year of Publication : 2024
Authors : Suman Chintala
DOI :  10.14445/22312803/IJCTT-V72I7P110

How to Cite?

Suman Chintala, "Boost Call Center Operations: Google's Speech-to-Text AI Integration," International Journal of Computer Trends and Technology, vol. 72, no. 7, pp.83-86, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I7P110

Abstract
A positive customer service experience can significantly boost a company's growth by fostering customer loyalty, reducing customer churn, and strengthening brand value. However, improving the efficiency of this process through automation and AI/ML still needs to be explored. This paper presents a solution for call centers that enhances customer support efficiency and uncovers business insights from audio data by integrating Google's Speech-to-Text API with the call center database (Big Query) and conducting post-call analytics using Looker Studio, aiming to revolutionize call center operations by enhancing efficiency and uncovering valuable business insights.

Keywords
Artificial Intelligence, Generative AI, Machine Learning, Business intelligence, Call center operations, Speech-toText, Sentiment analysis, Google cloud, Looker studio, Data analytics.

Reference

[1] Amazon, CCI Post Call Analytics Solution. [Online]. Available: https://aws.amazon.com/machine-learning/ml-use-cases/contact-center-intelligence/post-call-analytics-pca/
[2] Google Cloud, The ML. TRANSCRIBE Function. [Online]. Available: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-transcribe
[3] Google Cloud, The ML. UNDERSTAND_TEXT Function. [Online]. Available: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-understand-text
[4] Goutam Chakraborty, Murali Pagolu, and Satish Garla, Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS, SAS Institute, 2014.
[Google Scholar]
[5] Awni Hannun et al., “DeepSpeech: Scaling up end-to-end Speech Recognition,” arXiv, 2014.
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