Abstract: A sentence type recognition method and apparatus, an electronic device, and a storage medium are provided. The method includes: determining a target sentence from a to-be-recognized classroom teaching speech; segmenting the target sentence into a plurality of words, and processing a word vector of each of the plurality of words, to obtain semantic representation information of the target sentence; calculating, when the plurality of candidate sentence types comprise only a question and a non-question, an inner product of the plurality of pieces of dimension information and normalizing the inner product, to obtain a probability that the sentence type corresponding to the semantic representation information is the question; and determining, based on the probability, that the sentence type corresponding to the semantic representation information is the question or the non-question; determining the sentence type corresponding to the semantic representation information as a sentence type of the target sentence.

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.

Abstract: The present disclosure relates to a method and an apparatus for object preference prediction, and a computer readable medium. The method includes: acquiring evaluation information indicating preference values of partial users in a user set for partial objects in an object set; acquiring auxiliary information of at least one of the user set and the object set, wherein the auxiliary information indicates an attribute of at least one of a corresponding user in the user set and a corresponding object in the object set; determining a user feature representation and an object feature representation using a matrix decomposition model, based on the evaluation information and the auxiliary information; and determining a preference prediction value of a target user in the user set for a target object in the object set based on the user feature representation and the object feature representation.

Abstract: A sentence type recognition method and apparatus, an electronic device, and a storage medium are provided. The method includes: determining a target sentence from a to-be-recognized classroom teaching speech; segmenting the target sentence into a plurality of words, and processing a word vector of each of the plurality of words, to obtain semantic representation information of the target sentence; calculating, when the plurality of candidate sentence types comprise only a question and a non-question, an inner product of the plurality of pieces of dimension information and normalizing the inner product, to obtain a probability that the sentence type corresponding to the semantic representation information is the question; and determining, based on the probability, that the sentence type corresponding to the semantic representation information is the question or the non-question; determining the sentence type corresponding to the semantic representation information as a sentence type of the target sentence.

Abstract: The present disclosure provides a speaker recognition method, an electronic device, and a storage medium. An implementation includes: segmenting the target audio file and the to-be-recognized audio file into a plurality of audio units respectively; extracting an audio feature from each of the audio units to obtain an audio feature sequence of the target audio file and an audio feature sequence of the to-be-recognized audio file; performing feature learning on the audio feature sequence of the target audio file and the audio feature sequence of the to-be-recognized audio file by using Siamese neural network, to obtain a feature vector corresponding to the target audio file and feature vectors respectively corresponding to the plurality of audio units in the to-be-recognized audio file; and recognizing, by using an attention mechanism-based machine learning model, the audio units belonging to the target speaker in the to-be-recognized audio file based on the feature vectors.