Overview
This research investigates query-conditioned topic localization within long transcripts. The objective is to identify the specific sentence span that most effectively addresses a given topic-title query. The proposed method focuses on integrating speech information into this localization process without requiring a separate audio encoder, aiming to enhance the accuracy of span prediction.
Research Context
Long transcripts pose a challenge for downstream Natural Language Processing (NLP) systems due to their considerable input size and the frequent presence of irrelevant contextual information. The task of query-conditioned topic localization directly addresses this by seeking to pinpoint the most pertinent segment of a transcript in response to a specific query. Existing approaches often rely solely on textual analysis. This work explores the utility of incorporating speech-derived features to augment textual embeddings, thereby providing a richer representation for topic localization.
Approach
The core of the methodology involves reusing Automatic Speech Recognition (ASR) encoder states. These states, which encapsulate speech information, are utilized as sentence-level representations. To integrate this speech information with textual context, these ASR encoder states are fused with textual embeddings. This fusion creates a combined representation that is then fed into lightweight span locators. The primary motivation for this approach is to enable these span locators to leverage speech-derived information without incurring the computational overhead of running an additional, dedicated audio encoder. The system therefore capitalizes on information already generated during the ASR process.
Findings
- Experiments were conducted across two public datasets to evaluate the effectiveness of the proposed approach.
- The integration of reused ASR encoder states consistently yielded gains over baselines that relied solely on text for topic localization.
- These gains were particularly noticeable and robust when assessed against strict boundary-matching criteria, indicating an improved precision in identifying the exact span boundaries.
- Cross-dataset experiments provided further insights into the performance characteristics of the method:
- The benefits derived from incorporating speech information were strongest when applied to structured or semi-structured speech data.
- Conversely, the observed gains were limited and mixed when the method was applied to spontaneous speech.
Why This Matters
The ability to accurately localize specific topics within lengthy transcripts can reduce the computational burden on downstream NLP systems by providing more concise and relevant inputs. By reusing existing ASR encoder states, this method offers a resource-efficient way to integrate speech-derived contextual cues, potentially improving the effectiveness of systems that process spoken language without requiring extensive architectural changes or additional processing steps for audio features.