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Glottal Mixture Model (GLOMM) for speaker identification on telephone channels

: Baggenstoss, Paul; Wilkinghoff, K.; Kurth, F.

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European Association for Signal Processing -EURASIP-:
25th European Signal Processing Conference, EUSIPCO 2017 : 27 August - 2 September 2017, Kos Island, Greece
Kos, 2017
ISBN: 978-0-9928626-7-1
European Signal Processing Conference (EUSIPCO) <25, 2017, Kos>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FKIE ()

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.