Speakerfilter-pro: An Improved Target Speaker Extractor Combines The Time Domain And Frequency Domain

Abstract

This paper introduces an improved target speaker extractor, referred to as Speakerfilter-Pro, based on our previous Speakerfilter model. The Speakerfilter uses a bi-direction gated recurrent unit (BGRU) module to characterize the target speaker from anchor speech and use a convolutional recurrent network (CRN) module to separate the target speech from a noisy signal.Different from the Speakerfilter, the Speakerfilter-Pro sticks a WaveUNet module in the beginning and the ending, respectively. The WaveUNet has been proven to have a better ability to perform speech separation in the time domain. In order to extract the target speaker information better, the complex spectrum instead of the magnitude spectrum is utilized as the input feature for the CRN module. Experiments are conducted on the two-speaker dataset (WSJ0-mix2) which is widely used for speaker extraction. The systematic evaluation shows that the Speakerfilter-Pro outperforms the Speakerfilter and other baselines, and achieves

Stats

• citations5
• S2 citations—
• github stars0
• HF likes0
• heat score5.84
• arxiv keyhe2020speakerfilter

Related papers

• Speaker-conditioning Single-channel Target Speaker Extraction Using Conformer-based Architectures (2022)6.34
• Voicefilter: Targeted Voice Separation By Speaker-conditioned Spectrogram Masking (2018)17.48
• Improving Speaker Discrimination Of Target Speech Extraction With Time-domain Speakerbeam (2020)14.76
• Spectron: Target Speaker Extraction Using Conditional Transformer With Adversarial Refinement (2024)0.00
• 3S-TSE: Efficient Three-stage Target Speaker Extraction For Real-time And Low-resource Applications (2023)5.24
• Dual-path Filter Network: Speaker-aware Modeling For Speech Separation (2021)3.58
• Multi-stage Speaker Extraction With Utterance And Frame-level Reference Signals (2020)12.54
• Target Confusion In End-to-end Speaker Extraction: Analysis And Approaches (2022)9.59