Utterance Clustering Using Stereo Audio Channels

Yingjun Dong*, Neil G. MacLaren, Yiding Cao, Francis J. Yammarino, Shelley D. Dionne, Michael D. Mumford, Shane Connelly, Hiroki Sayama, Gregory A. Ruark

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- A nd right-channel audio signals in a few different ways and then by extracting the embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter-sharing Gaussian mixture model was obtained to train the model for each speaker. In the testing phase, the speaker with the maximum likelihood was selected as the detected speaker. Results of experiments with real audio recordings of multiperson discussion sessions showed that the proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono-audio signals in more complicated conditions.

Original languageEnglish
Article number6151651
JournalComputational Intelligence and Neuroscience
Publication statusPublished - 2021
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)
  • Neuroscience(all)
  • Mathematics(all)


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