@InProceedings{8081708,
  author = {P. M. Baggenstoss and K. Wilkinghoff and F. Kurth},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Glottal mixture model (GLOMM) for speaker identification on telephone channels},
  year = {2017},
  pages = {2734-2738},
  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.},
  keywords = {feature extraction;mixture models;speaker recognition;GLOMM;telephone channels;speaker-dependent voice source information;speech data;speaker identification performance;MFCC;error rate;telephone quality audio;error reduction;glottal mixture model;speaker-related information;I-vector approach;UBM approach;NTIMIT data set;Mel frequency cepstral coefficient;Speech;Feature extraction;Signal processing algorithms;Telephone sets;Europe},
  doi = {10.23919/EUSIPCO.2017.8081708},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570343401.pdf},
}

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