At a customer service center, thousands of hours of audio are generated. This audio provides a wealth of information to transcribe and analyze. With the additional data of the most successful customer service representatives, machine learning models can be trained to identify which speech patterns are associated with a successful worker.

By identifying these speaking patterns, a customer service center can continuously improve, with the different representatives learning the different patterns. The same is true for other speech-based tasks, such as sales calls.

Cresta is a company that builds systems to ingest high volumes of speech data in order to discover features that correlate with high performance human workers. Zayd Enam is a co-founder of Cresta, and joins the show to talk about the domain of speech data and what he and his team are building at Cresta.

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The post Cresta: Speech ML for Calls with Zayd Enam appeared first on Software Engineering Daily.

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