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A. Vuppala

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#natural language process... Preprint Sep 2026

V\={a}kQA: A Benchmark and Evaluation Study for Telugu Spoken Factoid Question Answering

V\={a}kQA, a Telugu SQA benchmark of 2,001 factoid question-answer pairs across six domains, is introduced and it is observed that Telugu phrasing retains cultural specificity that is lost in translation, speech input introduces phonetic confusions that alter question meaning, and cascaded ASR-MT errors compound progre...

Bhavana Akkiraju, Ravi Sastry Kolluru, D. SriCharan et al. · 0 citations
Conference Jul 2026

Phoneme-Preserving Mamba-Based Frontend for Noise-Robust Automatic Speech Recognition

Speech enhancement models optimised for perceptual quality metrics such as PESQ do not necessarily improve automatic speech recognition (ASR) performance, as aggressive noise suppression can distort the phonemic cues that acoustic models rely on. We propose a time-domain speech frontend based on the Mamba selective sta...

Reshma K. R., A. Vuppala · 0 citations
Conference Jul 2026

ResGDS-HLF: Residual Gumbel Dimension Selection with Hierarchical Layer Fusion for Stuttering Event Classification

Current systems for stuttering detection are limited by task-agnostic feature compression and flat layer-fusion strategies. Traditional methods rely on Principal Component Analysis (PCA) to reduce self-supervised representations, which often suppresses subtle disfluency patterns in favor of speakeridentity cues. We pro...

Pragya Khanna, Swathi Sambangi, Vijaya Saraswathi R et al. · 0 citations
Conference Jul 2026

QMOS: Qwen-Based MOS Prediction for TTS

Text-to-speech systems are improving fast, but measuring how natural they sound still requires expensive human listening tests. Existing automatic methods struggle to generalise well across different datasets. We present QMOS, a MOS prediction framework that extracts hierarchical speech quality features from a frozen Q...

Ravi Sastry Kolluru, S. Devarakonda, S. Radhe Shyam Salopanthula et al. · 0 citations
Conference Jul 2026

RG-RCT: Real-Governed Representation-Calibrated Training for Low-Resource Medical ASR

Developing Automatic Speech Recognition (ASR) systems for specialized medical domains is challenging in lowresource settings due to the limited availability of annotated speech data. Synthetic speech generated using text to speech (TTS) systems is often used to augment training data, but directly mixing synthetic and r...

S. Devarakonda, Ravi Sastry Kolluru, Srihari Bandarupalli et al. · 0 citations

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