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<p>Speech enhancement improves intelligibility and quality of noisy signals but remains challenging at low signal to noise ratio (SNR) and under real-time constraints. Time-domain approaches perform end-to-end waveform modeling, with many recent methods achieving multi-scale feature extraction. Most rely on dilation variation, temporal-only scaling, or sequential multi-scale modules with attention and gating, which improve accuracy but increase complexity and hinder real-time use. Therefore, our contribution is an inception temporal convolutional neural network called IncepTCN, a lightweight encoder–decoder architecture that integrates a parallel multi-kernel inception module into a causal dilated TCNN to capture fine-grained speech details and broader context. By using different convolutional sizes within a single module, the model sees speech at multiple scales simultaneously, improving separation from complex, non-stationary noise. Experiments on the Microsoft Scalable Noisy Speech Dataset (MS-SNSD) show that the proposed IncepTCN model outperforms conventional TCNs, achieving improvements of up to 0.53 in the Perceptual Evaluation of Speech Quality (PESQ), 9.14% in the Short-Time Objective Intelligibility (STOI), and 24.1&#xa0;dB in the Segmental Signal-to-Noise Ratio (SSNR), while generalizing effectively to unseen noise types and low-SNR conditions. Furthermore, the proposed IncepTCN maintains computational efficiency with 5.754M trainable parameters and supports real-time inference with low computational overhead.</p>

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IncepTCN: A Multi-Scale Inception-Temporal Architecture for Contextual Feature Learning in Raw Speech Enhancement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Harleen Kaur,
  • Asutosh Kar,
  • Shoba Sivapatham,
  • Balwinder Raj,
  • Vladimir Mladenovic

摘要

Speech enhancement improves intelligibility and quality of noisy signals but remains challenging at low signal to noise ratio (SNR) and under real-time constraints. Time-domain approaches perform end-to-end waveform modeling, with many recent methods achieving multi-scale feature extraction. Most rely on dilation variation, temporal-only scaling, or sequential multi-scale modules with attention and gating, which improve accuracy but increase complexity and hinder real-time use. Therefore, our contribution is an inception temporal convolutional neural network called IncepTCN, a lightweight encoder–decoder architecture that integrates a parallel multi-kernel inception module into a causal dilated TCNN to capture fine-grained speech details and broader context. By using different convolutional sizes within a single module, the model sees speech at multiple scales simultaneously, improving separation from complex, non-stationary noise. Experiments on the Microsoft Scalable Noisy Speech Dataset (MS-SNSD) show that the proposed IncepTCN model outperforms conventional TCNs, achieving improvements of up to 0.53 in the Perceptual Evaluation of Speech Quality (PESQ), 9.14% in the Short-Time Objective Intelligibility (STOI), and 24.1 dB in the Segmental Signal-to-Noise Ratio (SSNR), while generalizing effectively to unseen noise types and low-SNR conditions. Furthermore, the proposed IncepTCN maintains computational efficiency with 5.754M trainable parameters and supports real-time inference with low computational overhead.