Convolutional neural network (CNN) has been applied widely in various fields. However, it is always hindered by the unexplainable characteristics. Users cannot know why a CNN-based model produces certain recognition results, which is a vulnerability of CNN from the security perspective. To alleviate this problem, in this study, the three existing feature visualization methods of CNN are analyzed in detail firstly, and a unified visualization framework for interpreting the recognition results of CNN is presented. Here, class activation weight (CAW) is considered as the most important factor in the framework. Then, the different types of CAWs are further analyzed, and it is concluded that a linear correlation exists between them. Finally, on this basis, a spatial-channel attention-based class activation mapping (SCA-CAM) method is proposed. This method uses different types of CAWs as attention weights and combines spatial and channel attentions to generate class activation maps, which is capable of using richer features for interpreting the results of CNN. Experiments on four different networks are conducted. The results verify the linear correlation between different CAWs. In addition, compared with the existing methods, the proposed method SCA-CAM can effectively improve the visualization effect of the class activation map with higher flexibility on network structure.

Recent advancements in end-to-end (E2E) modeling have notably improved automatic speech recognition (ASR) systems; however, far-field speech recognition (FSR) remains challenging due to signal degradation from factors such as low signal-to-noise ratio, reverberation, and interfering sounds. This requires richer training data and multi-channel speech enhancement. To address this gap, we introduce Mixer, a novel data augmentation technique designed to further enhance the performance of large-scale pre-trained models for FSR. Mixer interpolates and mixes feature representations of speech samples and their corresponding losses, extending the MixSpeech framework to intermediate layers of Whisper. Additionally, we propose Mixer-C, which further leverages multi-channel information by combining speech from different microphone channels using a channel selector. Experimental results demonstrate that Mixer significantly outperforms existing methods, including SpecAugment, achieving a relative word error rate (WER) reduction of 3.6% compared to the baseline. Furthermore, Mixer-C offers an additional WER improvement of 2.2%, showcasing its efficacy in improving FSR accuracy.

Recent advancements in large-scale pre-trained automatic speech recognition (ASR) foundation models (e.g., Whisper) have exhibited remarkable performance in speech processing tasks. A recently emerging paradigm, prompt tuning, offers a parameter-efficient approach for fine-tuning, which has proven to be effective in enhancing the adaptation of pre-trained models to downstream tasks. In this paper, we first explore the prompting method for low-resource speech recognition based on Whisper. Although effective, it poses a challenge in the few-shot scenario due to its high sensitivity to initialization. To address this problem, we propose a novel meta-prompt for low-resource speech recognition that leverages the benefits of meta-learning for fast learning. Moreover, we further present a lightweight version of meta-prompt that omits the learning of encoder-prompt, reducing computational and storage costs. Extensive experiments on FLEURS datasets demonstrate consistent improvements across eleven target languages, showing better generalizability. Notably, meta-prompt achieves similar performance with a 20 $\%$ -shot compared to prompt tuning with a 50 $\%$ -shot setting, suggesting excellent few-shot learning ability.

Large language models produce semantically rich embeddings, yet direct employment of generic LLM embeddings fails to satisfy news recommendation demands due to inherent semantic mismatches with task targets. Existing fine-tuning methods including LoRA can narrow such gaps but bring prohibitive computational overhead, restricting real-world deployment. This work proposes lightweight task-adaptive modules (TAMs). It keeps LLM parameters fixed and transforms offline embeddings into task-specialized representations without full-model backpropagation, drastically cutting training costs. Evaluated on MIND benchmarks across ten mainstream recommendation architectures, TAMs achieves comparable accuracy to LoRA, with computational cost reduced to 1/10 of LoRA’s level. TAMs outperform GloVe-based models by 1.3–12.4% in AUC and scale effectively to MINDlarge. Ablation experiments confirm that the nonlinear projection is pivotal to performance improvement, and statistical validation across three random seeds confirms result robustness. This paradigm provides an efficient low-cost solution for LLM-based news recommendation under resource constraints.