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Shifa Yousaf

Publications and source records attributed to Shifa Yousaf.

3 recordsLinked to original sources

"I Wrote, I Paused, I Rewrote": A Mixed-Methods Study of Learner Perceptions of Process-Aware LLM Feedback

Large language models increasingly provide formative writing feedback, but most systems evaluate only the submitted text. Process-aware systems can additionally incorporate intermediate drafts and interaction records, yet access to these traces does not establish whether learners perceive the resulting feedback as useful, fair, or aligned with their own revision experience. We conducted a convergent mixed-method post-task evaluation with 43 undergraduate English-language learners. Participants completed a timed essay, received LLM-generated feedback based on the final essay, intermediate snapshots, and keystroke-derived records, and then answered six five-point Likert items and four open-ended questions. Ratings were concentrated above the neutral midpoint across all six items. Between 74.4% and 95.3% of participants selected ratings of 4 or 5, and all six one-sided exact sign tests remained significant after Holm correction. Perceived correspondence between the revision analysis and the participant's reported thought process was positively associated with overall perceived usefulness, Spearman's $ρ_s=.327$, 95% bootstrap CI $[.009,.609]$, $p=.032$, $n=43$. Qualitative responses were analysed through an LLM-assisted codebook development procedure followed by independent human coding of 14 binary codes. Participants frequently described the product feedback and revision rationale as accurate, while a smaller set reported misinterpretation of surface corrections, time constraints, or rhetorical intention. The findings distinguish perceived detection of revision activity from perceived interpretation of why revision occurred.

cs.HC↗

Domain-Specific Evaluation of Text-to-Speech Systems: A Multi-Metric Benchmarking Study

Recent advances in neural text-to-speech (TTS) systems have substantially improved speech naturalness and intelligibility across many languages. However, comprehensive evaluation methodologies that jointly assess perceptual quality, speaker similarity, and acoustic fidelity across diverse speech domains remain limited, particularly for low-resource and underrepresented languages. This paper presents a reproducible, multi-metric benchmarking framework for systematic evaluation of modern TTS systems through domain-specific analysis. The proposed framework integrates complementary subjective and objective evaluation protocols and is demonstrated through a comprehensive case study on a representative low-resource language spanning four speech domains: Formal, Conversational, Literary/Storytelling, and Emotional. Four state-of-the-art TTS systems -- Indic-Parler-TTS, MMS-TTS, Microsoft Edge TTS, and Google Gemini TTS -- are evaluated using MUSHRA listening tests, ABX discrimination tests, speaker similarity scoring with Resemblyzer, and acoustic analyses based on mel-cepstral distortion (MCD) and F0 RMSE over 960 audio pairs. Results reveal substantial variation in TTS performance across speech domains, with emotional speech consistently presenting the greatest synthesis challenge (mean MCD 12.03 dB; mean F0 RMSE 889 cents), while conversational speech achieves the highest overall acoustic fidelity. Beyond the empirical findings, this work provides a reproducible evaluation framework, publicly releasing evaluation scripts, result tables, and executable Colab notebooks to support standardized benchmarking and future research on TTS evaluation for low-resource languages.

cs.CL↗

"Can You See Me Think?" Grounding LLM Feedback in Keystrokes and Revision Patterns

As large language models (LLMs) increasingly assist in evaluating student writing, researchers have begun questioning whether these models can be cognitively grounded, that is, whether they can attend not just to the final product, but to the process by which it was written. In this study, we explore how incorporating writing process data, specifically keylogs and time-stamped snapshots, affects the quality of LLM-generated feedback. We conduct an ablation study on 52 student essays comparing feedback generated with access to only the final essay (C1) and feedback that also incorporates keylogs and time-stamped snapshots (C2). While rubric scores changed minimally, C2 feedback demonstrated significantly improved structural evaluation and greater process-sensitive justification.

cs.HC↗