SAGE: Switch-Aware EEG-Guided Soft Gating for Target Speaker Extraction with In-Trial Switching
This paper presents a new method for extracting a specific person's voice from a mix of voices using brain activity signals. The method, called SAGE, helps to improve the clarity of the target voice even when attention shifts during conversation.
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- 1 The final training objective combines these losses, including the uncertainty-weighted smoothness in Eq. 7, with appropriate weights.
- 2 Researchers have explored spatial information, visual cues, and biologically inspired mechanisms as alternative auxiliary cues to reduce dependence on enrollment speech.
- 3 We employ a soft selection mechanism based on a time window to predict the dynamic change of the target speaker based on the EEG signal.
- 4 We predict a switch probability psw(t) and an attention bias logit \u03b1(t) to indicate whether attention is changing and which candidate is more likely attended.
Introduction
The cocktail party problem describes the human ability to focus on a target talker in complex multi-speaker environments through selective auditory attention. Target speaker extraction (TSE) leverages auxiliary cues to separate the attended speech signal from a mixture.
EEG tracks the dynamics of attention in real time with millisecond-level temporal resolution.
Deep learning advances have substantially improved the performance of Target Speaker Extraction (TSE).
Prior studies do not provide dedicated mechanisms for spontaneous in-trial attention switching and cannot address performance degradation caused by endogenous, dynamic attention switches.
Future work will explore cross-subject generalization and robustness under more diverse acoustic conditions.
Research Question
The final training objective combines these losses, including the uncertainty-weighted smoothness in Eq. 7, with appropriate weights.
Methodology
We propose a novel dynamic target speaker extraction method based on EEG signals to overcome the limitations of traditional methods in handling in-trial target speaker switching. The participants consisted of 18 healthy native Mandarin-speaking adults with normal hearing.
Study Design
The auditory stimuli consisted of spatialized mixed speech from one male and one female speakers presented at +90 \u2022 and -90 \u2022 azimuths.
Participants were instructed to switch attention spontaneously and signal the switch using a button press.
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Results & Findings
Electroencephalography (EEG) provides a direct measure of a listener’s neural activity and cognitive state. Researchers have explored spatial information, visual cues, and biologically inspired mechanisms as alternative auxiliary cues to reduce dependence on enrollment speech.
- Electroencephalography (EEG) provides a direct measure of a listener’s neural activity and cognitive state.
- Researchers have explored spatial information, visual cues, and biologically inspired mechanisms as alternative auxiliary cues to reduce dependence on enrollment speech.
- Pan et al. proposed NeuroHeed+ to significantly improve neuro-guided speaker extraction by jointly modeling auditory attention decoding.
- Fan et al. introduced DGSD to improve feature learning accuracy by applying dynamic-graph self-distillation to EEG-based auditory spatial attention decoding.
- These properties degrade the reliability of neural decoding around switching moments and reduce the accuracy of detecting and tracking attention shifts.
Researchers have explored spatial information, visual cues, and biologically inspired mechanisms as alternative auxiliary cues to reduce dependence on enrollment speech.
We employ a soft selection mechanism based on a time window to predict the dynamic change of the target speaker based on the EEG signal.
Architecture
The framework consists of two main parts: speech separation and EEG attention regulation. The EEG attention regulation module includes EEG feature extractor, switch-aware soft gating, uncertainty-driven conservative strategy, and latency compensation alignment. The model outputs the extracted target waveform that follows the attended speaker even during attention switches.
Latency Compensation and Uncertainty Strategy
This section discusses the intrinsic latency discrepancies in EEG patterns and introduces a differentiable time-alignment module to compensate for these delays. Additionally, an uncertainty-driven conservative strategy is implemented to manage unreliable EEG cues.
Switch-aware Soft Gating
The switch-aware soft gating mechanism introduces smoother transitions by incorporating switch probabilities and temperature scaling, allowing for continuous evolution of attention rather than discrete changes. This helps to avoid discontinuities at switching points.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1 :: Figure 1: The overall framework of the proposed SAGE, switch-aware EEG-guided soft gating for target speaker extraction.
- 4. 1 .: Comparison with Baseline MethodsTo evaluate our EEG-based target speaker extraction approach, we compared it with several baseline models. As shown in Table 1, our method, SAGE, outperformed all baselines across all metrics on the spontaneous attention-switching dataset. SAGE achieved 8.67 dB in SI-SDR, surpassing BASEN (4.02 dB), NeuroHeed (4.96 dB), NeuroSpex+ (6.21 dB), and M3ANet (7.13 dB), demonstrating better interference suppression and speech distortion reduction for higher reconstruction quality. In terms of speech intelligibility, SAGE reached a STOI score of 88.24%, outperforming BASEN (74.83%), NeuroHeed (79.57%), NeuroSpex+ (82.84%), and M3ANet (84.30%), preserving perceptually important speech components for clearer, more intelligible extraction. SAGE achieved the lowest average switching latency of 2.04 s, outperforming BASEN (2.93 s), NeuroHeed (2.81 s), NeuroSpex+ (2.56 s), and M3ANet (2.37 s). This advantage underscored the effectiveness of the latency-compensated alignment module in alleviating the temporal mismatch between EEG attention labels and actual auditory attention switching, enabling faster and more reliable tracking. Overall, the proposed SAGE framework delivered superior performance over multiple strong baselines, with particularly pronounced gains during dynamic target switching.
- Figure 2 :: Figure 2: Ablation analysis of average switch latency. ( * * * : pvalue < 0.001 vs. SAGE Full).
Frequently Asked Questions
EEG signals are inherently noisy, non-stationary, and sensitive to environmental interference and inter-subject variability. The final training objective combines these losses, including the uncertainty-weighted smoothness in Eq. 7, with appropriate weights.
Participants were instructed to switch attention spontaneously and signal the switch using a button press. The experiments were conducted in a soundproof room with stimuli presented through headphones at a sound pressure level of 65 dB SPL.
Researchers have explored spatial information, visual cues, and biologically inspired mechanisms as alternative auxiliary cues to reduce dependence on enrollment speech. We employ a soft selection mechanism based on a time window to predict the dynamic change of the target speaker based.
The overall framework consists of speech separation and EEG attention regulation.
Prior studies do not provide dedicated mechanisms for spontaneous in-trial attention switching and cannot address performance degradation caused by endogenous, dynamic attention switches. Future work will explore cross-subject generalization and robustness under more diverse acoustic conditions.
This paper presents a new method for extracting a specific person’s voice from a mix of voices using brain activity signals. The method, called SAGE, helps to improve the clarity of the target voice even when attention shifts during conversation.