3S-TSE: Efficient Three-stage Target Speaker Extraction For Real-time And Low-resource Applications
2023 · Shulin He, Jinjiang Liu, Hao Li, et al.
Abstract
Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are computational intensive and impractical for real-time applications, espetially on resource-constrained platforms. In this paper, we address the TSE task using microphone array and introduce a novel three-stage solution that systematically decouples the process: First, a neural network is trained to estimate the direction of the target speaker. Second, with the direction determined, the Generalized Sidelobe Canceller (GSC) is used to extract the target speech. Third, an Inplace Convolutional Recurrent Neural Network (ICRN) acts as a denoising post-processor, refining the GSC output to yield the final separated speech. Our approach delivers superior performance while drastically reducing computational load, setting a new standard for efficient
Authors
(none)
Tags
Stats
Related papers
- Speakerbeam-ss: Real-time Target Speaker Extraction With Lightweight Conv-tasnet And State Space Modeling (2024)7.16
- Lightweight Speech Enhancement Guided Target Speech Extraction In Noisy Multi-speaker Scenarios (2025)0.00
- Focus On The Sound Around You: Monaural Target Speaker Extraction Via Distance And Speaker Information (2023)7.81
- X-crossnet: A Complex Spectral Mapping Approach To Target Speaker Extraction With Cross Attention Speaker Embedding Fusion (2024)0.00
- Continuous Target Speech Extraction: Enhancing Personalized Diarization And Extraction On Complex Recordings (2024)3.58
- X-tasnet: Robust And Accurate Time-domain Speaker Extraction Network (2020)10.48
- New Insights On Target Speaker Extraction (2022)0.00
- TSELM: Target Speaker Extraction Using Discrete Tokens And Language Models (2024)0.00