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- W2765960024 abstract "The Glottal Mixture Model (GLOMM) extracts speaker-dependent voice source information from speech data. It has previously been shown to provide speaker identification performance on clean speech comparable to universal background model (UBM), a state of the art method based on MFCC. And, when combined with UBM, the error rate was reduced by a factor of three, showing that the voice source information is largely independent of the information contained in the MFCC, yet holds as much speaker-related information. We now describe how GLOMM can be adapted for telephone quality audio and provide significant error reduction when combined with UBM and I-vector approaches. We demonstrate a factor of two error reduction on the NTIMIT data set with respect to the best published results." @default.
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- W2765960024 date "2017-08-01" @default.
- W2765960024 modified "2023-09-24" @default.
- W2765960024 title "Glottal mixture model (GLOMM) for speaker identification on telephone channels" @default.
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- W2765960024 doi "https://doi.org/10.23919/eusipco.2017.8081708" @default.
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