Abstract: The present disclosure provides data-driven methods and apparatuses for predicting user inquiries. One exemplary method includes: collecting user behavior data and pre-processing the user behavior data when a user inquiry is received; extracting candidate user behavior data that is contributive to the user inquiry from the pre-processed user behavior data; screening the candidate user behavior data based on a set target behavior data set, and selecting candidate user behavior data that is contained in the target behavior data set; inputting the screened candidate user behavior data into a trained classifier model; and predicting an inquiry category to which the user inquiry belongs. One exemplary apparatus includes a pre-processing module, an extraction module, and a prediction module. The method and the apparatus embodiments of the present disclosure can improve the efficiency and accuracy of the prediction.

Abstract: Embodiments of the disclosure provide methods and apparatuses processing audio data. The method can include: acquiring audio data by an audio capturing device, determining feature information of an enclosure in which the audio capturing device is located, and reverberating the feature information into the audio data.

Abstract: The present disclosure provides data-driven methods and apparatuses for predicting user inquiries. One exemplary method includes: collecting user behavior data and pre-processing the user behavior data when a user inquiry is received; extracting candidate user behavior data that is contributive to the user inquiry from the pre-processed user behavior data; screening the candidate user behavior data based on a set target behavior data set, and selecting candidate user behavior data that is contained in the target behavior data set; inputting the screened candidate user behavior data into a trained classifier model; and predicting an inquiry category to which the user inquiry belongs. One exemplary apparatus includes a pre-processing module, an extraction module, and a prediction module. The method and the apparatus embodiments of the present disclosure can improve the efficiency and accuracy of the prediction.

Abstract: Embodiments of the disclosure provide methods and apparatuses processing audio data. The method can include: acquiring audio data by an audio capturing device, determining feature information of an enclosure in which the audio capturing device is located, and reverberating the feature information into the audio data.

Abstract: Embodiments of the disclosure provide methods and apparatuses processing audio data. The method can include: acquiring audio data by an audio capturing device, determining feature information of an enclosure in which the audio capturing device is located, and reverberating the feature information into the audio data.

Abstract: The present disclosure provides data-driven methods and apparatuses for predicting user inquiries. One exemplary method includes: collecting user behavior data and pre-processing the user behavior data when a user inquiry is received; extracting candidate user behavior data that is contributive to the user inquiry from the pre-processed user behavior data; screening the candidate user behavior data based on a set target behavior data set, and selecting candidate user behavior data that is contained in the target behavior data set; inputting the screened candidate user behavior data into a trained classifier model; and predicting an inquiry category to which the user inquiry belongs. One exemplary apparatus includes a pre-processing module, an extraction module, and a prediction module. The method and the apparatus embodiments of the present disclosure can improve the efficiency and accuracy of the prediction.

Abstract: A speech recognition method includes clustering feature vectors of training data to obtain clustered feature vectors of training data performing interpolation calculation on feature vectors of data to be recognized using the clustered feature vectors of training data, and inputting the feature vectors of data to be recognized after the interpolation calculation into a speech recognition model to adaptively adjust the speech recognition model. The techniques of the present disclosure improve speech recognition accuracy and adaptive processing efficiency.

Abstract: Embodiments of the disclosure provide methods and apparatuses processing audio data. The method can include: acquiring audio data by an audio capturing device, determining feature information of an enclosure in which the audio capturing device is located, and reverberating the feature information into the audio data.

Abstract: Problem prediction method and system are provided. The prediction method includes receiving a request sent by a client, and obtaining a track of user activities of the client, the track of user activities including at least one of: call information of at least one RPC between the client and a server in a specified time period, or at least one URL of the server accessed by the client; extracting model input data from the track of user activities; and inputting the model input data into a problem classification model to predict a problem. The present disclosure uses a problem classification model to predict a problem, extracts model input data from a track of user activities as features for predicting the problem. In a process of prediction of a problem, manual operations are reduced, and the accuracy of the prediction is improved, while the timeliness is guaranteed at the same time.

Abstract: A method and an apparatus of extracting voiceprint information based on neural network are disclosed. The method includes: extracting a phonetic acoustic feature from an input voice segment; inputting the phonetic acoustic feature into a voiceprint model trained based on a neural network, and extracting a bottleneck feature of the neural network in the voiceprint model; and mapping frame vectors of the bottleneck feature of the neural network into a single-frame voiceprint expression vector, which serves as voiceprint information corresponding to the input voice segment. The neural network-based voiceprint information extraction method and apparatus extract voiceprint information of a voice segment using a voiceprint model trained based on a neural network, and thus the extraction process is relatively simple, and a short-time voice segment can be processed in a better manner.

Abstract: The present disclosure provides data-driven methods and apparatuses for predicting user inquiries. One exemplary method includes: collecting user behavior data and pre-processing the user behavior data when a user inquiry is received; extracting candidate user behavior data that is contributive to the user inquiry from the pre-processed user behavior data; screening the candidate user behavior data based on a set target behavior data set, and selecting candidate user behavior data that is contained in the target behavior data set; inputting the screened candidate user behavior data into a trained classifier model; and predicting an inquiry category to which the user inquiry belongs. One exemplary apparatus includes a pre-processing module, an extraction module, and a prediction module. The method and the apparatus embodiments of the present disclosure can improve the efficiency and accuracy of the prediction.

Abstract: The present application discloses speech recognition methods and apparatuses. An exemplary method may include extracting, via a first neural network, a vector containing speaker recognition features from speech data. The method may also include compensating bias in a second neural network in accordance with the vector containing the speaker recognition features. The method may further include recognizing speech, via an acoustic model based on the second neural network, in the speech data.

Abstract: A speech recognition method includes clustering feature vectors of training data to obtain clustered feature vectors of training data performing interpolation calculation on feature vectors of data to be recognized using the clustered feature vectors of training data, and inputting the feature vectors of data to be recognized after the interpolation calculation into a speech recognition model to adaptively adjust the speech recognition model. The techniques of the present disclosure improve speech recognition accuracy and adaptive processing efficiency.

Abstract: A method and an apparatus of extracting voiceprint information based on neural network are disclosed. The method includes: extracting a phonetic acoustic feature from an input voice segment; inputting the phonetic acoustic feature into a voiceprint model trained based on a neural network, and extracting a bottleneck feature of the neural network in the voiceprint model; and mapping frame vectors of the bottleneck feature of the neural network into a single-frame voiceprint expression vector, which serves as voiceprint information corresponding to the input voice segment. The neural network-based voiceprint information extraction method and apparatus extract voiceprint information of a voice segment using a voiceprint model trained based on a neural network, and thus the extraction process is relatively simple, and a short-time voice segment can be processed in a better manner.