Dialpad, Inc.

Etienne Manderscheid

James Palmer

Kevin James

Jonathan Johnston

Nicholas Foster

Eddie Yee Tak Ma

Stefan Roesch

Audio data saved at the end of client interactions are sampled, analyzed for pauses in speech, and sliced into stretches of acoustic data containing human speech between those pauses. The acoustic data are accompanied by machine transcripts made by VoiceAI. A suitable distribution of data useful for training and testing are stipulated during data sampling by applying certain filtering criteria. The resulting datasets are sent for transcription by a human transcriber team. The human transcripts are retrieved, some post-transcription processing and cleaning are performed, and the results are added to datastores for training and testing an acoustic model.

Maintainable and scalable pipeline for automatic speech recognition language modeling

The content and/or sound characteristics of audio calls are monitored in real-time. The occurrence of triggers during audio calls is automatically detected, and in response directives are displayed to parties in real-time while calls are occurring. Multiple triggers and directives can be defined and edited, either by human users and/or automatically (e.g., in response to results obtained and tracked over time). Each given directive corresponds to one or more given triggers, such that detection of an occurrence of a given trigger during a monitored audio call results in the automatic outputting of a corresponding directive.

Automatically providing directives to parties to audio calls in real-time while the calls are occurring

An AI platform is fully integrated with existing VoIP/AVoIP telephony infrastructure. In the course of providing VoIP/AVoIP audiovisual calls, a VoIP/AVoIP media stream of audiovisual content is processed, and transferred between endpoints. AI processing is applied to each frame of the transferred audiovisual content, in real-time while the audiovisual call is occurring. For example, automated speech recognition can be performed on the content, in which the speech of the audiovisual content is converted to text. The audiovisual call can further be automatically transcribed to a text file in real-time. Another example is the automatic detection of the occurrence of specific triggers during calls. Additional enhanced functionality is automatically provided as a result of applying the AI processing to the transferred audiovisual content. For example, in response to detecting the occurrence of a specific trigger, a corresponding directive can be automatically output on a screen of a calling device.

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San Francisco, CA, United States of America

Etienne Manderscheid