Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models
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Abstract (arXiv)
Recent advancements in multi-turn voice interaction models have improved user-model communication. However, while closed-source models effectively retain and recall past utterances, whether open-source models share this ability remains unexplored. To fill this gap, we systematically evaluate how well open-source interaction models utilize past utterances using ContextDialog, a benchmark we proposed for this purpose. Our findings show that speech-based models have more difficulty than text-based ones, especially when recalling information conveyed in speech, and even with retrieval-augmented generation, models still struggle with questions about past utterances. These insights highlight key limitations in open-source models and suggest ways to improve memory retention and retrieval robustness.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2025.
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Citing papers per year: 2025: 3; 2026: 6.
9 papers.
- 2025Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Surveycited by 38
- 2025MULTI-Bench: A Multi-Turn Interactive Benchmark for Assessing Emotional Intelligence ability of Spoken Dialogue Modelscited by 9
- 2026The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Eracited by 5
- 2026Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Modelscited by 2
- 2026SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noisecited by 1
- 2026Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversationscited by 1
- 2026From Awareness to Adherence: Bridging the Context Gap in Spoken Dialogue Systems via Context-Aware Decodingcited by 1
- 2025Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Modelscited by 0
- 2026Beyond Dyadic Memory: Interaction-Aware Multimodal Memory with Adaptive Agentic Retrieval for Multi-Party Spoken Conversationscited by 0
- 2026Beyond Dyadic Memory: Interaction-Aware Multimodal Memory with Adaptive Agentic Retrieval for Multi-Party Spoken Conversations26 Sep 2026
- 2026Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Models18 Sep 2026
- 2026From Awareness to Adherence: Bridging the Context Gap in Spoken Dialogue Systems via Context-Aware Decoding15 Jun 2026
- 2026Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations11 Jun 2026
- 2026SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise13 Feb 2026
- 2026The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era9 Jan 2026
- 2025Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models29 Dec 2025
- 2025MULTI-Bench: A Multi-Turn Interactive Benchmark for Assessing Emotional Intelligence ability of Spoken Dialogue Models2 Nov 2025
- 2025Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey21 May 2025
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- 2026VoiceLongMemEval: Do Assistants Remember How You Sounded?
- 2021Beyond Goldfish Memory: Long-Term Open-Domain Conversation
- 2026Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents
- 2026S3-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants
- 2025In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
- 2026When Users Don't Ask: Benchmarking Context-Driven Memory Retrieval in Conversational Agents
- 2025Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction
- 2026PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations