# Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models

arXiv: https://arxiv.org/abs/2502.19759 · HTML: https://mlsanity.com/paper/2502.19759/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2025.

## Built on by (most cited first, 9 of 9)

- [Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2505.15957/) (2025)
- [MULTI-Bench: A Multi-Turn Interactive Benchmark for Assessing Emotional Intelligence ability of Spoken Dialogue Models](https://mlsanity.com/paper/2511.00850/) (2025)
- [The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era](https://mlsanity.com/paper/2601.05564/) (2026)
- [Vox-Infinity: Benchmarking the Limits of Long-Context Spoken Language Models](https://mlsanity.com/paper/2609.22452/) (2026)
- [SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise](https://mlsanity.com/paper/2602.12783/) (2026)
- [Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations](https://mlsanity.com/paper/2606.13464/) (2026)
- [From Awareness to Adherence: Bridging the Context Gap in Spoken Dialogue Systems via Context-Aware Decoding](https://mlsanity.com/paper/2606.16472/) (2026)
- [Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models](https://mlsanity.com/paper/2512.23578/) (2025)
- [Beyond Dyadic Memory: Interaction-Aware Multimodal Memory with Adaptive Agentic Retrieval for Multi-Party Spoken Conversations](https://mlsanity.com/paper/2609.32522/) (2026)

## 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.