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RingCentral Acquires DeepAffects Conversational Intelligence Pioneer to Enable Smarter Video Meetings
25 Dec, 2020
Creates more engaging meeting experience with emotion recognition, real-time closed captioning, and automatic speaker recognition
BELMONT, CA - December 21, 2020 - RingCentral, Inc. (NYSE: RNG), a leading provider of global enterprise cloud communications, video meetings, collaboration, and contact center solutions, today announced that it has acquired DeepAffects, a leading conversational intelligence pioneer that uses artificial intelligence (AI) to analyze business conversations and extract meaningful insights. The acquisition is part of RingCentral’s rapid innovation to create smarter video meetings. DeepAffects brings powerful AI capabilities that will enable RingCentral to deliver enhanced pre-meeting, in-meeting and post-meeting experiences for customers. “As businesses shift to a hybrid model with employees working both in-office and remotely, it has never been more important to equip them with tools and insights that make meetings more engaging and productive,” said Anand Eswaran, president and chief operating officer at RingCentral. “With DeepAffects, we will bring conversational speech analysis and emotional sentiment recognition to our platform to deliver a differentiated, best-in-class meetings experience for our customers. We’re thrilled to welcome the DeepAffects team to RingCentral!” The DeepAffects platform includes some of the following key capabilities:- Multi-modal emotion recognition: Recognizes core emotions and sentiments directly from voice. It categorizes the conversations on the emotion cues, such as intensity, pitch and more, to bring emotional awareness insights to participants.
- Multi-speaker recognition and voiceprints: Identifies an individual person based on the unique characteristics of their voice. It also identifies the speaker at precisely the time they spoke during the conversation.
- Speech recognition with accent detection:Converts audio to text by applying powerful neural network models. It recognizes multiple languages and variants to support a global user base with an accent-aware speech recognition that can transcribe a non-native English speaker’s audio with very high accuracy.
