Topic-to-Timestamp Alignment by Constrained Evidence Selection

• URL: http://arxiv.org/abs/2606.20890v1
• Date: Thu, 18 Jun 2026 19:38:57 GMT
• Title: Topic-to-Timestamp Alignment by Constrained Evidence Selection
• Abstract summary: Given a natural-language topic and a timestamped meeting transcript, the goal is to return the time at which the topic is discussed.<n>A standard RAG setup can retrieve relevant transcript excerpts, but still asks the language model to generate a timestamp.<n>We recast timestamp prediction as constrained temporal candidate selection.
• Score: 5.582495870293832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meeting archives are difficult to search when users remember what was discussed but not when. We study topic-to-timestamp alignment: given a natural-language topic and a timestamped meeting transcript, the goal is to return the time at which the topic is discussed. A standard RAG setup can retrieve relevant transcript excerpts, but still asks the language model to generate a timestamp, which can produce unsupported or invalid timecodes. We therefore recast timestamp prediction as constrained temporal candidate selection: the system retrieves timestamped transcript chunks, and the model selects the candidate that best grounds the topic instead of generating a timecode. On 420 topic-timestamp queries from 200 municipal meeting transcripts, this increases Recall@5 from 31.9% to 50.0%, reduces MAE from 837.0 seconds to 761.0 seconds with Mistral-7B-Instruct, and increases the number of parseable outputs from 373 to 419 of 420 queries. The results suggest that temporal grounding in long transcripts depends strongly on retrieval quality and output design, not only on the choice of the language model.

Related papers

• GigaChat Audio: Time-aware Large Audio Language Model [65.51589021112385]
  We present a time-aware audio LLM that answers questions with explicit timestamps over up to 120 minutes of input.<n>Our approach interleaves periodic time markers with continuous audio tokens using large-scale synthetic supervision from a cascaded pipeline.
  arXiv  Detail & Related papers  (2026-07-11T16:34:25Z)
• REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing [46.75114860324604]
  Autoregressive ASR systems can emit timestamps as decoded tokens, enabling timestamped transcription without frame-level or inference-time post-processing.<n>We show that these generated timestamps can drift across long non-speech spans.<n>We propose REDDIT(REplay-based Distribution eDITing), a lightweight two-stage post-training framework that corrects timestamps while avoiding forgetting.
  arXiv  Detail & Related papers  (2026-07-06T17:40:54Z)
• In-Sync: Adaptation of Speech Aware Large Language Models for ASR with Word Level Timestamp Predictions [51.236148875603185]
  We extend an existing speech-aware language model to predict timestamps directly alongside transcripts.<n>We introduce a set of novel lightweight training strategies that improve alignment robustness while preserving recognition quality.
  arXiv  Detail & Related papers  (2026-04-14T20:56:24Z)
• LongAudio-RAG: Event-Grounded Question Answering over Multi-Hour Long Audio [6.935416517354558]
  LongAudio Generation (LARAG) is a framework that grounds Large Language Model (LLM) outputs in retrieved, timestamped acoustic event detections.<n>We demonstrate the practicality of our approach by deploying it in a hybrid edge-cloud environment.<n>Experiments show that structured, event-level retrieval significantly improves accuracy compared to vanilla Retrieval-Augmentment.
  arXiv  Detail & Related papers  (2026-02-16T10:15:22Z)
• TimeStampEval: A Simple LLM Eval and a Little Fuzzy Matching Trick to Improve Search Accuracy [0.0]
  TimeStampEval is a benchmark for retrieving precise millisecond timestamps from long transcripts given non-verbatim quotes.<n>Our simple two-stage method dramatically improves retrieval accuracy while cutting inference costs by over 90%.<n>The motivating use case is an automated long-form podcast that assembles Congressional Record clips into AI-hosted verbatim narration.
  arXiv  Detail & Related papers  (2025-10-27T21:54:56Z)
• Universal Video Temporal Grounding with Generative Multi-modal Large Language Models [59.781211641591405]
  This paper presents a computational model for universal video temporal grounding, which accurately localizes temporal moments in videos based on natural language queries.<n>We propose UniTime, a robust and universal video grounding model leveraging the strong vision-language understanding capabilities of generative Multi-modal Large Language Models (MLLMs)<n>Our model effectively handles videos of diverse views, genres, and lengths while comprehending complex language queries.
  arXiv  Detail & Related papers  (2025-06-23T17:53:18Z)
• Word Level Timestamp Generation for Automatic Speech Recognition and Translation [28.176210372699618]
  We introduce a data-driven approach for enabling word-level timestamp prediction in the Canary model.<n>Our method demonstrates precision and recall rates between 80% and 90%, with timestamp prediction errors ranging from 20 to 120 ms across four languages.
  arXiv  Detail & Related papers  (2025-05-21T15:24:29Z)
• Long-Form Speech Generation with Spoken Language Models [64.29591880693468]
  Textless spoken language models struggle to generate plausible speech past tens of seconds.<n>We derive SpeechSSM, the first speech language model family to learn from and sample long-form spoken audio.<n>SpeechSSMs leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency.
  arXiv  Detail & Related papers  (2024-12-24T18:56:46Z)
• Handling Numeric Expressions in Automatic Speech Recognition [56.972851337263755]
  We compare cascaded and end-to-end approaches to recognize and format numeric expressions.<n>Results show that adapted end-to-end models offer competitive performance with the advantage of lower latency and inference cost.
  arXiv  Detail & Related papers  (2024-07-18T09:46:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.