Baggenstoss, PaulPaulBaggenstossWilkinghoff, K.K.WilkinghoffKurth, F.F.Kurth2022-03-132022-03-132017https://publica.fraunhofer.de/handle/publica/3982132-s2.0-850415289372-s2.0-85041706510The 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.enconference paper