Abstract: An intent recognition method, an intent recognition apparatus, a storage medium and a computer device are provided, aiming to improve the accuracy and the efficiency of the intent recognition, which relates to the technical field of information. The method includes: acquiring user's audio to be recognized; determining multi-frame audio feature vectors corresponding to the audio to be recognized; inputting the multi-frame audio feature vectors into a preset intent recognition model to obtain multiple output sequences corresponding to the multi-frame audio feature vectors, wherein the output sequences comprise blank characters and non-blank characters; and determining a target intent corresponding to the audio to be recognized based on the multiple output sequences. The present disclosure is suitable for recognizing the intent of the user.

Abstract: A method for evaluating a speech forced alignment model, an electronic device, and a storage medium are provided. The method includes: according to each audio segment in a test set and a text corresponding to each audio segment, acquiring, by using a speech forced alignment model to be evaluated, a phoneme sequence corresponding to each audio segment and a predicted start time and a predicted end time of each phoneme in the phoneme sequence; for each phoneme, obtaining a time accuracy score of the phoneme according to the predicted start time and the predicted end time of the phoneme and a predetermined reference start time and a predetermined reference end time of the phoneme; and determining a time accuracy score of said speech forced alignment model according to the time accuracy score of each phoneme.

Abstract: A method for evaluating a speech forced alignment model, an electronic device, and a storage medium are provided. The method includes: according to each audio segment in a test set and a text corresponding to each audio segment, acquiring, by using a speech forced alignment model to be evaluated, a phoneme sequence corresponding to each audio segment and a predicted start time and a predicted end time of each phoneme in the phoneme sequence; for each phoneme, obtaining a time accuracy score of the phoneme according to the predicted start time and the predicted end time of the phoneme and a predetermined reference start time and a predetermined reference end time of the phoneme; and determining a time accuracy score of said speech forced alignment model according to the time accuracy score of each phoneme.