2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-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
SessionAUD-1: Audio and Speech Source Separation 1: Speech Separation
LocationGather.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
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.