2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPE-18.6 | ||
| Paper Title | DON’T SHOOT BUTTERFLY WITH RIFLES: MULTI-CHANNEL CONTINUOUS SPEECH SEPARATION WITH EARLY EXIT TRANSFORMER | ||
| Authors | Sanyuan Chen, Harbin Institute of Technology, China; Yu Wu, Zhuo Chen, Takuya Yoshioka, Shujie Liu, Jinyu Li, Microsoft Corporation, China; Xiangzhan Yu, Harbin Institute of Technology, China | ||
| Session | SPE-18: Speech Enhancement 4: Multi-channel Processing | ||
| Location | Gather.Town | ||
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 | ||
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 | ||
| Presentation | Poster | ||
| Topic | Speech Processing: [SPE-ENHA] Speech Enhancement and Separation | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | With its strong modeling capacity that comes from a multi-head and multi-layer structure, Transformer is a very powerful model for learning a sequential representation and has been successfully applied to speech separation recently. However, multi-channel speech separation sometimes does not necessarily need such a heavy structure for all time frames especially when the cross-talker challenge happens only occasionally. For example, in conversation scenarios, most regions contain only a single active speaker, where the separation task downgrades to a single speaker enhancement problem. It turns out that using a very deep network structure for dealing with signals with a low overlap ratio not only negatively affects the inference efficiency but also hurts the separation performance. To deal with this problem, we propose an early exit mechanism, which enables the Transformer model to handle different cases with adaptive depth. Experimental results indicate that not only does the early exit mechanism accelerate the inference, but it also improves the accuracy. | ||