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Behavioral Signals Listed as a Sample Vendor in 2021 Gartner Hype Cycle for Natural Language Technologies Report

  • According to This Gartner Report, “Emotion AI Is Considered Transformational as It Turns Human Behavioral Attributes Into Data That Will Have a Large Impact on Human-Machine Interface (Hmi).”

Behavioral Signals, a leader in emotion artificial intelligence for speech recognition technology, based in Los Angeles, CA, announced that it has been identified as a Sample Vendor in the 2021 Gartner Hype Cycle™ for Natural Language Technologies.[1] . Behavioral Signals was named in the Emotion AI category.

“Our sole focus for the past 5 years has been on working closely with our clients, to develop and integrate Emotion AI based technology, AI-MC, in their call center software platforms, strongly contributing in corporate growth and profitability,” said Rana Gujral, CEO of Behavioral Signals. “Traditional NLP and speech recognition offerings focus on ‘what’ is being said. We take it a step further and introduce the ability to also understand ‘how’ it is being said. We capture and measure vocal cues from conversations, focusing strongly on emotions and behaviors, which allow us to better understand human-to-human interactions. That means we can use Emotion AI to match each call center customer to the best-suited agent to drive the conversation. This has resulted in a 12 to 18% increase in revenues, for every single client of ours.”

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Behavioral Signal Processing is based on over a decade’s worth of award-winning and patented research and automatically detects information that is encoded in the human voice from audio to measure the quality of human interaction. Their AI-Mediated Conversation technology is built with this technology (BSP), allowing companies to introduce Emotion AI into their applications to offer a rich variety of emotional and behavioral recognition metrics.

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The Gartner report says about Emotion AI, “Machines will become more “humanized” as they can detect sentiments in many different contexts. Furthermore, applying deep learning to computer vision or audio-based systems to analyze emotions in real time has spawned new use cases for customer experience enhancements, employee wellness and many other areas”. The report further states that “Strongest adoption is currently happening in the context of contact centers where voice-based emotion analysis supports multiple use cases such as real-time analysis on voice conversations, emotion detection in chat conversations, emotional chatbots and more.”

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