SearcharxivSearch

arXiv · 2508.15088

Comparative Evaluation of Text and Audio Simplification: A Methodological Replication Study

Abstract

This study serves as a methodological replication of Leroy et al. (2022) research, which investigated the impact of text simplification on healthcare information comprehension in the evolving multimedia landscape. Building upon the original studys insights, our replication study evaluates audio content, recognizing its increasing importance in disseminating healthcare information in the digital age. Specifically, we explored the influence of text simplification on perceived and actual difficulty when users engage with audio content automatically generated from that text. Our replication involved 44 participants for whom we assessed their comprehension of healthcare information presented as audio created using Leroy et al. (2022) original and simplified texts. The findings from our study highlight the effectiveness of text simplification in enhancing perceived understandability and actual comprehension, aligning with the original studys results. Additionally, we examined the role of education level and language proficiency, shedding light on their potential impact on healthcare information access and understanding. This research underscores the practical value of text simplification tools in promoting health literacy. It suggests the need for tailored communication strategies to reach diverse audiences effectively in the healthcare domain.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Prosanta Barai, Gondy Leroy, Arif Ahmed. 2025-08-20. Comparative Evaluation of Text and Audio Simplification: A Methodological Replication Study. https://doi.org/10.17705/1atrr.00001

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD