How can the proposed hierarchical graph modeling approach be extended to incorporate additional modalities, such as acoustic features, to further improve the performance of automated speaking assessment systems?
The proposed hierarchical graph modeling approach can be significantly enhanced by integrating additional modalities, particularly acoustic features, to create a more comprehensive assessment of spoken language proficiency. Acoustic features, such as pitch, tone, speech rate, and prosody, provide critical insights into a speaker's fluency and emotional expression, which are essential components of effective communication.
To achieve this integration, a multi-modal framework can be developed where acoustic features are represented as additional nodes within the existing hierarchical graph structure. For instance, each spoken response could be linked to its corresponding acoustic profile, allowing the model to analyze both the semantic content and the acoustic characteristics simultaneously. This could involve creating separate subgraphs for acoustic features that interact with the existing semantic and discourse relation graphs, thereby enriching the overall representation of each response.
Moreover, employing techniques such as attention mechanisms within the graph attention network (GAT) can help the model weigh the importance of different modalities dynamically. By training the model on a diverse dataset that includes both textual and acoustic data, it can learn to identify patterns and correlations between spoken content and acoustic features, leading to improved prediction accuracy in automated speaking assessments. This multi-modal approach not only enhances the robustness of the assessment system but also aligns with the naturalistic nature of human communication, where both verbal and non-verbal cues play a vital role.