The invention provides a media program interaction method and system and is applicable to the field of multimedia application The media program interaction method comprises the following steps: establishing an audio feature library by a server in real time; storing audio feature values and program interactive information of all media programs; recording an audio of a currently-played media program through a mobile device; extracting the audio feature values of the audio of the media program, and sending the extracted audio feature value to the server; carrying out similarity matching on the received audio feature value and the audio feature values of all the media programs in the audio feature library by the server, so as to identify the currently-played media program; and judging the type information of the currently-played media program by the server, and pushing the corresponding program interactive information to the mobile device according to the type information of the currently-played media program With the adoption of the media program interaction method and the media program interaction system provided by the invention, the media program interactive information watched by a user can be accurately and rapidly fed back to the user The media program interaction method and the media program interaction system provided by the invention have the advantages of large amount of the interactive information, various types and better user experience; and the various demands of the user can be met

Media program interaction method and system

The invention provides a method for recommending music stars with tones similar to those of singers. The method includes the steps that pure human voice frequencies are obtained, preprocessing is conducted on the pure human voice frequencies, a voice feature coefficient set of each pure human voice frequency is extracted, and corresponding music star models are trained through the voice model algorithm; preprocessing is conducted on given user voice samples, and feature coefficient sets are extracted; the feature coefficient sets of the user voice samples are matched with all the music star models, and the music stars with the tones most similar to the tones of the singers are found out. The invention further provides a corresponding device. The method and device for recommending the music stars with the tones similar to those of the singers can be applied to KTV scenes, are used for recommending music stars with the tones similar to those of users, can increase singing pleasure, and improve the level of the users for simulating the tones of the music stars.

Method and device for recommending music stars with tones similar to those of singers

The invention provides an identification method for intelligent robots in order to accurately identify personnel identity. The identification method includes the steps of 1), establishing an identification database; 2), inputting facial images and voice information to be identified; 3), respectively calculating feature vectors of inputted facial images and voices; 4), performing identification of faces and voices; 5), outputting identification results.

Identification method for intelligent robots

The embodiments of the invention provide a speech recognition method and a speech recognition device. The method comprises the following steps: acquiring a characteristic classification result of a speech signal to be recognized, wherein the characteristic classification result includes pronunciations used to describe the pronunciation characteristics of speech signal frames and the probability that the speech signal frames are mapped to the corresponding pronunciations; removing the pronunciations contained in the characteristic classification result through filtering based on the probability contained in the characteristic classification result; and recognizing the speech signal based on the characteristic classification result after filtering. According to the embodiments of the invention, there is no need to implement recognition operation related to the removed pronunciations in the process of speech signal recognition, for example, there is no need to search a recognition network for a path related to the removed pronunciations. Therefore, the time consumed by the speech recognition process can be reduced, and the efficiency of speech recognition can be improved.

Speech recognition method and speech recognition device

The invention discloses a voice interaction system and a voice interaction method. The voice interaction system comprises a preprocessing module, an acoustic model library, a language model library, an identification module and an interaction center, wherein the preprocessing module comprises a voice preprocessing module and an endpoint detection module; the acoustic model library comprises an HMMmodel matching unit, a TDNN model matching unit, an HMM model database and an ANN model database; the language model library comprises a N-Gram model database and a Rule-based model database; the identification module comprises an MFCC parameter feature extraction unit and an identification control unit; and the interaction center comprises a semantic understanding module, an interaction processing module, a response information library and a semantic dictionary database. The voice interaction system and the voice interaction method utilize a feedback module to monitor identification information and a feedback instruction of a client, demonstrate the identification information to the client by means of the identification control unit, control and change a voice matching model and the language model library, and optimize the interactive identification accuracy rate of the voice interaction system.

Voice interaction system and method

The invention discloses a voice recognition method facing a specific crowd. The method comprises the following steps of: first, sampling a voice signal and converting the voice signal to a digital signal from an analogue signal; then, pre-weighting, windowing, en-framing and performing front-end processing of endpoint detection on the digital voice signal; later on, performing feature extraction on the voice signal by adopting discrete wavelet transform; and finally, performing voice recognition on the feature-extracted voice signal by adopting a discrete hidden Markov model after training a sample. In the processes of performing the front-end processing and the feature extraction on the voice signal, spectrum features and pronunciation characteristics of different target crowds are fully taken into consideration and the process of extracting voice information is optimized, so that a processing process and an information extracting process can be simplified; and therefore, recognition precision is ensured, simultaneously calculation amount and information storage capacity in the recognition process are greatly reduced, and the voice recognition on an embedded platform is realized.

Juyang Xiong

Papers

Voice recognition method facing specific crowd