Navigating Speech Recording Collections with AI-Generated Illustrations
- URL: http://arxiv.org/abs/2507.04182v1
- Date: Sat, 05 Jul 2025 22:38:10 GMT
- Title: Navigating Speech Recording Collections with AI-Generated Illustrations
- Authors: Sirina Håland, Trond Karlsen Strøm, Petra Galuščáková,
- Abstract summary: We propose a novel navigational method for speech archives that leverages recent advances in language and multimodal generative models.<n>We demonstrate our approach with a Web application that organizes data into a structured format using interactive mind maps and image generation tools.<n>The system is implemented using the TED-LIUM3 dataset, which comprises over 2,000 speech transcripts and audio files of TED Talks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although the amount of available spoken content is steadily increasing, extracting information and knowledge from speech recordings remains challenging. Beyond enhancing traditional information retrieval methods such as speech search and keyword spotting, novel approaches for navigating and searching spoken content need to be explored and developed. In this paper, we propose a novel navigational method for speech archives that leverages recent advances in language and multimodal generative models. We demonstrate our approach with a Web application that organizes data into a structured format using interactive mind maps and image generation tools. The system is implemented using the TED-LIUM~3 dataset, which comprises over 2,000 speech transcripts and audio files of TED Talks. Initial user tests using a System Usability Scale (SUS) questionnaire indicate the application's potential to simplify the exploration of large speech collections.
Related papers
- A Cascaded Architecture for Extractive Summarization of Multimedia Content via Audio-to-Text Alignment [0.0]
This study presents a cascaded architecture for extractive summarization of multimedia content via audio-to-text alignment.<n>It integrates audio-to-text conversion using Microsoft Azure Speech with advanced extractive summarization models, including Whisper, Pegasus, and Facebook BART XSum.<n> Evaluation using ROUGE and F1 scores demonstrates that the cascaded architecture outperforms conventional summarization methods.
arXiv Detail & Related papers (2025-03-06T13:59:14Z) - Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models [83.7506131809624]
We introduce an approach to identifying speaker names in dialogue transcripts, a crucial task for enhancing content accessibility and searchability in digital media archives.
We present a novel, large-scale dataset derived from the MediaSum corpus, encompassing transcripts from a wide range of media sources.
We propose novel transformer-based models tailored for SpeakerID, leveraging contextual cues within dialogues to accurately attribute speaker names.
arXiv Detail & Related papers (2024-07-16T18:03:58Z) - Multi-Modal Retrieval For Large Language Model Based Speech Recognition [15.494654232953678]
We propose multi-modal retrieval with two approaches: kNN-LM and cross-attention techniques.
We show that speech-based multi-modal retrieval outperforms text based retrieval.
We achieve state-of-the-art recognition results on the Spoken-Squad question answering dataset.
arXiv Detail & Related papers (2024-06-13T22:55:22Z) - Auto-ACD: A Large-scale Dataset for Audio-Language Representation Learning [50.28566759231076]
We propose an innovative, automatic approach to establish an audio dataset with high-quality captions.
Specifically, we construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.5M audio-text pairs.
We employ LLM to paraphrase a congruent caption for each audio, guided by the extracted multi-modality clues.
arXiv Detail & Related papers (2023-09-20T17:59:32Z) - SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding
Tasks [88.4408774253634]
Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community.
There are not nearly as many SLU task benchmarks, and many of the existing ones use data that is not freely available to all researchers.
Recent work has begun to introduce such benchmark for several tasks.
arXiv Detail & Related papers (2022-12-20T18:39:59Z) - Self-Supervised Speech Representation Learning: A Review [105.1545308184483]
Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains.
Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods.
This review presents approaches for self-supervised speech representation learning and their connection to other research areas.
arXiv Detail & Related papers (2022-05-21T16:52:57Z) - Automated Audio Captioning: an Overview of Recent Progress and New
Challenges [56.98522404673527]
Automated audio captioning is a cross-modal translation task that aims to generate natural language descriptions for given audio clips.
We present a comprehensive review of the published contributions in automated audio captioning, from a variety of existing approaches to evaluation metrics and datasets.
arXiv Detail & Related papers (2022-05-12T08:36:35Z) - QuerYD: A video dataset with high-quality text and audio narrations [85.6468286746623]
We introduce QuerYD, a new large-scale dataset for retrieval and event localisation in video.
A unique feature of our dataset is the availability of two audio tracks for each video: the original audio, and a high-quality spoken description.
The dataset is based on YouDescribe, a volunteer project that assists visually-impaired people by attaching voiced narrations to existing YouTube videos.
arXiv Detail & Related papers (2020-11-22T17:33:44Z) - Unsupervised Pattern Discovery from Thematic Speech Archives Based on
Multilingual Bottleneck Features [41.951988293049205]
We propose a two-stage approach, which comprises unsupervised acoustic modeling and decoding, followed by pattern mining in acoustic unit sequences.
The proposed system is able to effectively extract topic-related words and phrases from the lecture recordings on MIT OpenCourseWare.
arXiv Detail & Related papers (2020-11-03T20:06:48Z)
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.