| Paper ID | AUD-1.6 |
| Paper Title |
MULTICHANNEL OVERLAPPING SPEAKER SEGMENTATION USING MULTIPLE HYPOTHESIS TRACKING OF ACOUSTIC AND SPATIAL FEATURES |
| Authors |
Aidan Hogg, Imperial College London, United Kingdom; Christine Evers, University of Southampton, United Kingdom; Patrick A. Naylor, Imperial College London, United Kingdom |
| Session | AUD-1: Audio and Speech Source Separation 1: Speech Separation |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-AMCT] Audio and Speech Modeling, Coding and Transmission |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
An essential part of any diarization system is the task of speaker segmentation which is important for many applications including speaker indexing and automatic speech recognition (ASR) in multi-speaker environments. Segmentation of overlapping speech has recently been a key focus of this work. In this paper we explore the use of a new multimodal approach for overlapping speaker segmentation that tracks both the fundamental frequency (F0) of the speaker and the speaker’s direction of arrival (DOA) simultaneously. Our proposed multiple hypothesis tracking system, which simultaneously tracks both features, shows an improvement in segmentation performance when compared to tracking these features separately. An illustrative example of overlapping speech demonstrates the effectiveness of our proposed system. We also undertake a statistical analysis on 12 meetings from the AMI corpus and show an improvement in the HIT rate of 14.1% on average against a commonly used deep learning bidirectional long short term memory networks (BLSTM) approach. |
