In the field of multimedia forensics，identifying the source of voice evidence is crucial for judicial applications and information security. Voice recordings often serve as key evidence in legal investigations，yet their evidential value is limited by the capabilities of current analytical techniques. Most existing approaches focus on identifying the mobile phone model but lack the precision to differentiate between individual devices of the same model. This limitation considerably reduces the utility of voice evidence because it cannot be uniquely linked to a specific device. As a result，such evidence is often treated as supplementary in court proceedings rather than being considered direct and legally valid proof. Fine-grained device identification inability hinders digital forensics effectiveness and highlights the need for advanced methodologies.

To address this challenge，this study proposes a novel voice source identification （VSI） model based on multiscale feature fusion and dynamic enhancement. The model is designed to capture and amplify the subtle hardware-specific fingerprints embedded in speech signals. These fingerprints arise from variations in microphone and circuit components，making them unique to each device. The proposed framework consists of several interconnected modules. First，a residual-attention collaborative network is employed to enhance feature representation. This network combines the strengths of residual learning and attention mechanisms，enabling the model to focus on device-related characteristics while suppressing irrelevant speech content and environmental noise. The residual connections facilitate training stability，while the attention mechanism dynamically weights important features，improving discriminability. Second，a dual-path feature extraction structure is implemented. The overall feature extraction module uses an expression-enhanced time delay neural network to capture broad，context-rich device characteristics. This module processes long-term temporal dependencies，which are essential for identifying model-level attributes. Simultaneously，a local feature extraction module based on a multilevel residual SE-Res2Net architecture is used to capture fine-grained，high-frequency details. This component excels at identifying minute differences between individual devices by leveraging hierarchical residual connections and channel-wise attention，enabling multiscale feature learning within a unified framework. Third，a feature enhancement module incorporating feature recalibration and dynamic global filtering is applied. This module refines the fused multiscale features by emphasizing task-relevant information and filtering out noise. Feature recalibration adjusts channel-wise feature responses adaptively，while dynamic global filtering performs content-aware smoothing and enhancement，further strengthening the device-specific representations. Finally，a fine-grained classification model is constructed to achieve cross-level device identification. This classifier is trained to distinguish devices at multiple hierarchical levels，from brand and model down to the individual unit，improving accuracy and generalization. The entire architecture is optimized end-to-end，ensuring seamless integration of all components.

To evaluate the effectiveness of the proposed VSI model comprehensively，a large-scale voice dataset comprising 14 mobile phone brands and 121 different individual devices was constructed. This dataset is designed to cover a wide range of scenarios and conditions，thereby providing a rigorous testbed for assessing the model's performance. The VSI model was evaluated using three key metrics： equal error rate （EER），accuracy （ACC），and minimum detection cost function （minDCF）. Results demonstrate the superior performance of the VSI model compared with existing methods. Specifically，the VSI model achieved an EER of 7.50%，an ACC of 89.97%，and a minDCF of 0.38. These results are remarkably better than those obtained by four other state-of-the-art methods reported in the literature. Compared with these methods，the EER was reduced by 4.75%，4.71%，5.44% and 3.46%，respectively. The ACC was increased by 3.98%，2.20%，4.90%，and 2.52% respectively，and the minDCF was decreased by 0.04，0.04，0.30，and 0.03，respectively. These improvements highlight the effectiveness of the VSI model in accurately identifying individual devices from voice signals. Moreover，the VSI model demonstrated robustness under varying conditions of voice duration，sampling rate，and encoding format. This robustness is crucial for practical applications because voice signals in real-world scenarios often exhibit variations in these parameters. The ability of the VSI model to maintain high performance across different conditions indicates its potential for widespread adoption in forensic and security-related applications.

The findings demonstrate that the proposed VSI model remarkably advances the field of voice source identification by enabling precise，individual-level device recognition. By effectively extracting and enhancing hardware-specific fingerprints from speech signals，the model provides a reliable means of linking voice recordings to specific mobile devices. This capability enhances the evidentiary value of voice data in judicial contexts，allowing it to serve as direct and valid evidence. Furthermore，the model’s robustness to real-world variations ensures its practical applicability in diverse scenarios. These contributions offer strong technical support for judicial forensics，intelligent terminal authentication，and broader domains of multimedia security. Future work will focus on extending the model to handle more complex audio manipulations and integrating it with complementary forensic techniques.