---
_id: '57085'
abstract:
- lang: eng
  text: We propose an approach for simultaneous diarization and separation of meeting
    data. It consists of a complex Angular Central Gaussian Mixture Model (cACGMM)
    for speech source separation, and a von-Mises-Fisher Mixture Model (VMFMM) for
    diarization in a joint statistical framework. Through the integration, both spatial
    and spectral information are exploited for diarization and separation. We also
    develop a method for counting the number of active speakers in a segment of a
    meeting to support block-wise processing. While the total number of speakers in
    a meeting may be known, it is usually not known on a per-segment level. With the
    proposed speaker counting, joint diarization and source separation can be done
    segment-by-segment, and the permutation problem across segments is solved, thus
    allowing for block-online processing in the future. Experimental results on the
    LibriCSS meeting corpus show that the integrated approach outperforms a cascaded
    approach of diarization and speech enhancement in terms of WER, both on a per-segment
    and on a per-meeting level.
author:
- first_name: Tobias
  full_name: Cord-Landwehr, Tobias
  id: '44393'
  last_name: Cord-Landwehr
- first_name: Christoph
  full_name: Boeddeker, Christoph
  id: '40767'
  last_name: Boeddeker
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Cord-Landwehr T, Boeddeker C, Haeb-Umbach R. Simultaneous Diarization and
    Separation of Meetings through the Integration of Statistical Mixture Models.
    In: <i>ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and
    Signal Processing (ICASSP)</i>. ; 2024. doi:<a href="https://doi.org/10.1109/ICASSP49660.2025.10888445">10.1109/ICASSP49660.2025.10888445</a>'
  apa: Cord-Landwehr, T., Boeddeker, C., &#38; Haeb-Umbach, R. (2024). Simultaneous
    Diarization and Separation of Meetings through the Integration of Statistical
    Mixture Models. <i>ICASSP 2025 - 2025 IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP)</i>. 2025 IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India. <a href="https://doi.org/10.1109/ICASSP49660.2025.10888445">https://doi.org/10.1109/ICASSP49660.2025.10888445</a>
  bibtex: '@inproceedings{Cord-Landwehr_Boeddeker_Haeb-Umbach_2024, title={Simultaneous
    Diarization and Separation of Meetings through the Integration of Statistical
    Mixture Models}, DOI={<a href="https://doi.org/10.1109/ICASSP49660.2025.10888445">10.1109/ICASSP49660.2025.10888445</a>},
    booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech
    and Signal Processing (ICASSP)}, author={Cord-Landwehr, Tobias and Boeddeker,
    Christoph and Haeb-Umbach, Reinhold}, year={2024} }'
  chicago: Cord-Landwehr, Tobias, Christoph Boeddeker, and Reinhold Haeb-Umbach. “Simultaneous
    Diarization and Separation of Meetings through the Integration of Statistical
    Mixture Models.” In <i>ICASSP 2025 - 2025 IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP)</i>, 2024. <a href="https://doi.org/10.1109/ICASSP49660.2025.10888445">https://doi.org/10.1109/ICASSP49660.2025.10888445</a>.
  ieee: 'T. Cord-Landwehr, C. Boeddeker, and R. Haeb-Umbach, “Simultaneous Diarization
    and Separation of Meetings through the Integration of Statistical Mixture Models,”
    presented at the 2025 IEEE International Conference on Acoustics, Speech and Signal
    Processing (ICASSP), Hyderabad, India, 2024, doi: <a href="https://doi.org/10.1109/ICASSP49660.2025.10888445">10.1109/ICASSP49660.2025.10888445</a>.'
  mla: Cord-Landwehr, Tobias, et al. “Simultaneous Diarization and Separation of Meetings
    through the Integration of Statistical Mixture Models.” <i>ICASSP 2025 - 2025
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>,
    2024, doi:<a href="https://doi.org/10.1109/ICASSP49660.2025.10888445">10.1109/ICASSP49660.2025.10888445</a>.
  short: 'T. Cord-Landwehr, C. Boeddeker, R. Haeb-Umbach, in: ICASSP 2025 - 2025 IEEE
    International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    2024.'
conference:
  location: Hyderabad, India
  name: 2025 IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP)
date_created: 2024-11-14T09:32:38Z
date_updated: 2025-08-14T08:12:22Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.1109/ICASSP49660.2025.10888445
file:
- access_level: closed
  content_type: application/pdf
  creator: cord
  date_created: 2025-08-14T08:11:57Z
  date_updated: 2025-08-14T08:11:57Z
  file_id: '60930'
  file_name: main.pdf
  file_size: 259907
  relation: main_file
  success: 1
file_date_updated: 2025-08-14T08:11:57Z
has_accepted_license: '1'
keyword:
- diarization
- source separation
- mixture model
- meeting
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/pdf/2410.21455
oa: '1'
project:
- _id: '52'
  name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
- _id: '508'
  name: Automatische Transkription von Gesprächssituationen
publication: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech
  and Signal Processing (ICASSP)
status: public
title: Simultaneous Diarization and Separation of Meetings through the Integration
  of Statistical Mixture Models
type: conference
user_id: '44393'
year: '2024'
...
---
_id: '11740'
abstract:
- lang: eng
  text: In this contribution we derive the Maximum A-Posteriori (MAP) estimates of
    the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations.
    We assume the distortion to be white Gaussian noise of known mean and variance.
    An approximate conjugate prior of the GMM parameters is derived allowing for a
    computationally efficient implementation in a sequential estimation framework.
    Simulations on artificially generated data demonstrate the superiority of the
    proposed method compared to the Maximum Likelihood technique and to the ordinary
    MAP approach, whose estimates are corrected by the known statistics of the distortion
    in a straightforward manner.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian
    Mixture Model in the Presence of Noisy Observations. In: <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:3352-3356.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>'
  apa: Chinaev, A., &#38; Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters
    of a Gaussian Mixture Model in the Presence of Noisy Observations. In <i>38th
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>
    (pp. 3352–3356). <a href="https://doi.org/10.1109/ICASSP.2013.6638279">https://doi.org/10.1109/ICASSP.2013.6638279</a>
  bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of
    the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>},
    booktitle={38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013},
    pages={3352–3356} }'
  chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
    Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.”
    In <i>38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)</i>, 3352–56, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638279">https://doi.org/10.1109/ICASSP.2013.6638279</a>.
  ieee: A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of
    a Gaussian Mixture Model in the Presence of Noisy Observations,” in <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013,
    pp. 3352–3356.
  mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters
    of a Gaussian Mixture Model in the Presence of Noisy Observations.” <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013,
    pp. 3352–56, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>.
  short: 'A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.'
date_created: 2019-07-12T05:27:20Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638279
keyword:
- Gaussian noise
- maximum likelihood estimation
- parameter estimation
- GMM parameter
- Gaussian mixture model
- MAP estimation
- Map-based estimation
- maximum a-posteriori estimation
- maximum likelihood technique
- noisy observation
- sequential estimation framework
- white Gaussian noise
- Additive noise
- Gaussian mixture model
- Maximum likelihood estimation
- Noise measurement
- Gaussian mixture model
- Maximum a posteriori estimation
- Maximum likelihood estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf
oa: '1'
page: 3352-3356
publication: 38th International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf
status: public
title: MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence
  of Noisy Observations
type: conference
user_id: '44006'
year: '2013'
...