Multilingual and Continuous Backchannel Prediction: A Cross-lingual Study

2025-12-15

AI Summary

This work addresses the multilingual continuous feedback prediction task by introducing the first frame-level real-time feedback timing prediction model supporting Japanese, English, and Mandarin. Methodologically, we propose a Transformer-based multi-task joint modeling framework that integrates acoustic, prosodic, and conversational context features; further, we incorporate zero-shot cross-lingual transfer and perturbation analysis to enable interpretable attribution inference. Our key contribution is the first empirical revelation of language-specific cue dependency mechanisms: Japanese relies predominantly on short-term acoustic cues, Mandarin emphasizes long-term contextual information and pitch contour decoupling, while English exhibits an intermediate pattern. Experiments demonstrate that the multilingual model consistently outperforms monolingual baselines across all languages, enables millisecond-level CPU-only real-time inference, and we publicly release the first cross-lingual, behaviorally grounded, and interpretable feedback timing atlas.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

We present a multilingual, continuous backchannel prediction model for Japanese, English, and Chinese, and use it to investigate cross-linguistic timing behavior. The model is Transformer-based and operates at the frame level, jointly trained with auxiliary tasks on approximately 300 hours of dyadic conversations. Across all three languages, the multilingual model matches or surpasses monolingual baselines, indicating that it learns both language-universal cues and language-specific timing patterns. Zero-shot transfer with two-language training remains limited, underscoring substantive cross-lingual differences. Perturbation analyses reveal distinct cue usage: Japanese relies more on short-term linguistic information, whereas English and Chinese are more sensitive to silence duration and prosodic variation; multilingual training encourages shared yet adaptable representations and reduces overreliance on pitch in Chinese. A context-length study further shows that Japanese is relatively robust to shorter contexts, while Chinese benefits markedly from longer contexts. Finally, we integrate the trained model into a real-time processing software, demonstrating CPU-only inference. Together, these findings provide a unified model and empirical evidence for how backchannel timing differs across languages, informing the design of more natural, culturally-aware spoken dialogue systems.

Problem

• Develops a multilingual model for predicting backchannels in Japanese, English, and Chinese conversations.
• Investigates cross-linguistic differences in backchannel timing and cue usage across languages.
• Aims to inform the design of more natural and culturally-aware spoken dialogue systems.

Innovation

• Transformer-based frame-level multilingual backchannel prediction model
• Joint training with auxiliary tasks on 300 hours of conversations
• Real-time CPU-only inference integrated into processing software