Utterance Clustering Using Stereo Audio Channels
This work addresses utterance clustering for audio signal processing, but it is incremental as it builds on existing methods by incorporating stereo data.
The study tackled utterance clustering by processing stereo audio signals to combine left and right channels, extracting d-vector features, and using a Gaussian mixture model for supervised clustering. It achieved significantly better performance than conventional mono methods in complex multi-person discussion conditions.
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- and right-channel audio signals in a few different ways and then extracted 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 conducted 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 multi-person 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.