Deep learning algorithms for speech interaction analysis

Abstract

This article offers a comparative assessment of deep learning algorithms for automatic analysis of verbal interactions, drawing upon evidence from n recent scientific literature. Four widely used algorithms –LSTM, CNN, GRU, and x-vectors– are analyzed across key dimensions relevant to speech analytics in collaborative contexts: speaker diarization accuracy (DER), ability to handle overlapping speech (SDR), robustness to noise as indicated by error recognition (WER), and computational efficiency assessed via inference time. Based on previously published empirical results, this synthesis evaluates the strengths, limitations, and optimal application scenarios for each algorithm, with a focus on educational and collaborative settings. The analysis shows that LSTMs excel in temporal modeling, CNNs achieve superior performance in voice separation, GRUs reach a favorable balance between accuracy and efficiency, and x-vectors enable highly scalable speaker identification. This study establishes a comprehensive framework for selecting and integrating deep learning techniques into speech analysis systems that demonstrate robustness and context-sensitivity across educational, professional, and research applications.