Name
Abstract | ||
|---|---|---|
During the last few years, the speaker recognition technique has been widely attractive for its extensive application in many fields, such as speech communications, domestics services, and smart terminals. As a critical method, the Gaussian mixture model (GMM) makes it possible to achieve the recognition capability that is close to the hearing ability of human in a long speech. However, the GMM is... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2018.2799928 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Speaker recognition,Speech,Feature extraction,Speech recognition,Spectrogram,Computational modeling,Informatics | Convergence (routing),Computer science,Spectrogram,Convolutional neural network,Word error rate,Utterance,Feature extraction,Real-time computing,Speech recognition,Speaker recognition,Mixture model | Journal |
Volume | Issue | ISSN |
14 | 7 | 1551-3203 |
Citations | PageRank | References |
16 | 0.60 | 0 |
Authors | ||
5 | ||