4 results about "Speech classification" patented technology

The present application belongs to the technical field of intelligent teaching, and particularly relates to a teaching quality evaluation method and system based on deep learning. The method comprises extracting feature data to be evaluated from normal speech, evaluating the feature data to be evaluated by using a speech evaluation model to generate an evaluation result, and determining the quality grade of the normal speech according to the evaluation result, wherein the feature extraction from noise speech and noise speech comprises extracting amplitude information and frequency information from a sound production section. The present application performs screening on teaching speech to identify abnormal sound sections; through voiceprint comparison, the teaching speech in which the abnormal sound sections that can match the pre-stored voiceprint are classified as noise speech, and the teaching speech that cannot be matched is classified as noise speech, so as to distinguish the noise speech originating from the background environment from the noise speech originating from the teaching subject, overcome the evaluation error problem caused by regarding the two as noise without distinction, and lay a data foundation for subsequent evaluation.

The application provides a speech classification model training method, a speech classification method and device, comprising: a plurality of sample speech segments obtained by segmenting and determining a speech stream with a plurality of speakers; inputting each sample speech segment into a pre-constructed speech classification model to generate a target feature vector, a first prediction result and a second prediction result; clustering each sample speech segment according to the target feature vector to obtain a plurality of clustering clusters, and sample speech segments with the same pseudo label belong to speech segments corresponding to the same speaker; calculating a first error of the pseudo label and the first prediction result; calculating a second error of the second prediction result and label information; and training the speech classification model according to the first error and the second error. According to the embodiment of the application, the accuracy of classification can be improved, so that the speakers corresponding to the plurality of speech segments in the speech stream can be clustered without waiting for the entire conversation to end.

The application relates to the communication technical field, and provides a seamless roaming method and system. The method comprises the following steps: receiving a first frame sent by a station; determining a target access point based on the first frame; sending context information of the station to the target access point, and receiving response information sent by the target access point; setting a value of an access category (AC) to which a second frame belongs as a value of a voice category (AC_VO), or setting a value of an AC to which the second frame belongs as a new value; and sending the second frame to the station. In the process of switching the access point, the value of the AC to which the second frame belongs is set as the value of the voice category (AC_VO), or the value of the AC to which the second frame belongs is set as the new value, so that, compared with other to-be-executed processes of a source access point, the sending process of the second frame can be preferentially executed by the source access point, thereby the interaction time length of the overall switching process can be reduced, the packet loss rate can be reduced, the service lag can be avoided, and the user experience can be improved.

This application provides a speech recognition method, a speech recognition system, a computer device, and a storage medium, belonging to the field of financial technology. The method includes: inputting target speech with a preset emotion category into a pre-trained multi-task speech recognition model; encoding the target speech using a first speech coding sub-model to obtain initial speech features; performing speech attention processing on the initial speech features using a first attention sub-model to obtain first target attention features; encoding the initial speech features using a second speech coding sub-model to obtain hidden speech features; performing hidden attention processing on the first target attention features and the hidden speech features using a second attention sub-model to obtain second target attention features; and performing speech classification on the second target attention features using a multi-task classification sub-model to obtain a target speech label. This application embodiment can improve the recognition accuracy of multi-task speech recognition.