DETECTING BREAKS IN SPEECH FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
개요
발명자
Myungjong Kim; Harishchandra Dubey; Utkarsh Vaidya; Oluwatobi Olabiyi
IPC 분류
CPC 분류
In various examples, detecting breaks in speech for conversational AI systems and applications is described herein. Systems and methods are disclosed herein that use both end of sentence detection and end of utterance detection associated with words from text (e.g., tokens) to determine when to further process various portions of the text. For instance, one or more models may process text data associated with the text, where the text data may be generated using an automatic speech recognition (ARS) model based on audio data representing speech. Based at least on processing the text data, the model(s) may generate and/or output data representing first indicators that the words are associated with ends of sentences, second indicators that the words are associated with ends of utterances, and third indicators that the words are not associated with either ends of sentences or ends of utterances.
원문 (중국어)
In various examples, detecting breaks in speech for conversational AI systems and applications is described herein. Systems and methods are disclosed herein that use both end of sentence detection and end of utterance detection associated with words from text (e.g., tokens) to determine when to further process various portions of the text. For instance, one or more models may process text data associated with the text, where the text data may be generated using an automatic speech recognition (ARS) model based on audio data representing speech. Based at least on processing the text data, the model(s) may generate and/or output data representing first indicators that the words are associated with ends of sentences, second indicators that the words are associated with ends of utterances, and third indicators that the words are not associated with either ends of sentences or ends of utterances.