What question did this study set out to answer?

This research aims to analyze the effects of English speaking anxiety on communicative competence using a neural network approach.

March 8, 2026Open Access

Analysis of the Influence of English Speaking Anxiety and Communicative Competence Based on a Neural Network Fusion Model

Puntos clave

This research aims to analyze the effects of English speaking anxiety on communicative competence using a neural network approach.
Proposed the MSATC co-attention-based multimodal fusion model.
Employed the speech S-ABHC model for deep acoustic feature extraction.
Utilized MFCCs, spectrograms, raw waveforms, BiGRU, and HuBERT for feature extraction.
Adapted the RoBERTa model to capture text sentiment.
Conducted experiments on the IEMOCAP dataset to evaluate performance.
Achieved 77.41% weighted accuracy in classification tasks.
Obtained 78.66% unweighted accuracy, indicating strong performance.
Outperformed existing multimodal models in the analysis.

Resumen

English-speaking anxiety significantly impacts learners’ communication, and automated diagnosis is crucial. Existing multimodal approaches face limitations in deep feature extraction and modality fusion. This study proposes MSATC, a co-attention-based multimodal fusion model. The speech S-ABHC model extracts deep acoustic features using MFCCs, spectrograms, raw waveforms, BiGRU, and HuBERT, while RoBERTa captures text sentiment. The co-attention module enables bidirectional, adaptive feature interaction, producing discriminative joint representations. Experiments on IEMOCAP show 77.41% weighted and 78.66% unweighted accuracy, outperforming existing models.

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