A speech breakpoint detection method, apparatus and device based on artificial intelligence. The method comprises: acquiring a query sentence input by a user (201); performing sentence recognition on the query sentence, and obtaining at least one candidate result of the query sentence and a probability corresponding to each candidate result (202); performing, through a pretrained semantic completeness model, semantic completeness detection on candidate results with a probability higher than a predetermined threshold (203); after it is determined that the candidate results with a probability higher than the predetermined threshold are semantically complete, performing natural language understanding on the candidate results with a probability higher than the predetermined threshold, and obtaining intentions corresponding to the candidate results with a probability higher than the predetermined threshold (204); and obtaining a response corresponding to the query sentence according to the candidate results with a probability higher than the predetermined threshold and the corresponding intentions (205). On the basis of a traditional acoustic model, the semantic completeness model is used for querying the query sentence input by the user, and whether the speech of the user ends is dynamically determined on the basis of the semantic completeness, so that the real intention of the user can be recognized more accurately, and whether the speech of the user ends can be accurately determined in scenarios such as repetitive speech and prolonged sound of the user, and thus, the user experience can be improved.
SPEECH BREAKPOINT DETECTION METHOD, APPARATUS AND DEVICE BASED ON ARTIFICIAL INTELLIGENCE