arXiv subjects

Susmita Bhattacharjee

Publications and source records attributed to Susmita Bhattacharjee.

4 recordsLinked to original sources

arXiv2026

Fairness Evaluation of Edge-AI Implementation for Cleft Lip and Palate Speech ASR

Automatic speech recognition (ASR) remains challenging for individuals with cleft lip and palate (CLP) because of limited pathological speech data and large variations in speech characteristics across speakers and severity levels. These recognition difficulties can reduce the accessibility of voice-based human-computer interaction, particularly when cloud-based ASR services are unavailable or unreliable. This work investigates a severity-aware and edge-deployable ASR framework for improving recognition of CLP speech using Whisper-small. The model was fine-tuned using different combinations of normal and CLP speech representing mild, moderate, and severe conditions, together with a CLP-only training configuration, to examine how the inclusion of different severity levels influences recognition performance and fairness across speakers. The pretrained model produced pooled word error rate (WER) and phoneme error rate (PER) values of 62.46% and 52.72%, respectively. Severity-aware fine-tuning substantially improved performance, reducing the best pooled WER to 22.72% and the best pooled PER to 18.44%. Training with a broader representation of CLP severity levels also provided the best overall balance between recognition accuracy and performance consistency across severity groups. Deployment on an NVIDIA Jetson platform demonstrated real-time inference for all fine-tuned models, with real-time factors of 0.167-0.171 and peak GPU memory usage of approximately 566 MB. The results demonstrate that incorporating severity diversity during ASR adaptation can substantially improve recognition of CLP speech while reducing performance disparities across severity groups. The proposed approach further enables low-latency, Internet-independent speech interaction on edge devices, supporting more accessible and inclusive voice-based human-computer interaction for individuals with CLP.

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arXiv2026

Normal-Anchored First-Order Model-Agnostic Meta-Learning based Whisper Fine-Tuning for Enhancing Fairness of Cleft Lip and Palate Speech Recognition

Automatic speech recognition (ASR) for cleft lip and palate (CLP) speech is difficult because acoustic and articulatory patterns vary across severity levels. This variability reduces the performance of pretrained ASR systems, and conventional fine-tuning may not generalize well under low-resource, heterogeneous CLP conditions. This work proposes Normal-Anchored First-Order Model-Agnostic Meta-Learning (NA-FOMAML) for adapting Whisper to CLP speech. The method uses a first-order bilevel meta-learning framework in which normal speech is used in the inner loop as a stable support condition, while CLP severity groups are used in the outer loop to improve post-adaptation robustness. This design aims to reduce the performance gap between normal and pathological speech. Experiments are conducted on the NMCPC and AIISH datasets using four normal-anchored training configurations. Frozen encoder, full encoder, and selected Whisper encoder-layer tuning strategies are evaluated, including layers 0--5, 4--11, 6--11, and 8--11, with decoder and projection-head adaptation. Results show that outer-loop training with only normal speech is insufficient. For NMCPC, full encoder tuning with Normal to Normal+Mild+Moderate gives WERs of 4.40%, 5.53%, 16.14%, and 52.07% for normal, mild, moderate, and severe speech. For AIISH, full encoder tuning with Normal to Normal+Mild+Moderate+Severe gives WERs of 2.48%, 19.66%, 14.05%, and 57.50%. A transcription-based phoneme-category analysis shows that severe CLP speech has high error rates across fricatives, affricates, nasals, liquids, plosives, and vowels. Overall, NA-FOMAML improves cross-severity robustness, but severe speech still requires severity-aware sampling, phoneme-aware loss functions, and augmentation targeting pressure consonant and resonance-related distortions.

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arXiv2025

Parameter-Efficient Fine-Tuning of Foundation Models for CLP Speech Classification

We propose the use of parameter-efficient fine-tuning (PEFT) of foundation models for cleft lip and palate (CLP) detection and severity classification. In CLP, nasalization increases with severity due to the abnormal passage between the oral and nasal tracts; this causes oral stops to be replaced by glottal stops and alters formant trajectories and vowel space. Since foundation models are trained for grapheme prediction or long-term quantized representation prediction, they may better discriminate CLP severity when fine-tuned on domain-specific data. We conduct experiments on two datasets: English (NMCPC) and Kannada (AIISH). We perform a comparative analysis using embeddings from self-supervised models Wav2Vec2 and WavLM, and the weakly supervised Whisper, each paired with SVM classifiers, and compare them with traditional handcrafted features eGeMAPS and ComParE. Finally, we fine-tune the best-performing Whisper model using PEFT techniques: Low-Rank Adapter (LoRA) and Decomposed Low-Rank Adapter (DoRA). Our results demonstrate that the proposed approach achieves relative improvements of 26.4% and 63.4% in macro-average F1 score over the best foundation model and handcrafted feature baselines on the NMCPC dataset, and improvements of 6.1% and 52.9% on the AIISH dataset, respectively.

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arXiv2025

Improving ASR Fairness for Cleft Lip and Palate Speech: A Study on Severity-Aware Data Mixing

Speech produced by individuals with cleft lip and palate (CLP) is often hypernasal (and sometimes breathy) due to structural anomalies, yielding shifts in formant structure that degrade automatic speech recognition (ASR) performance and fairness. Building on evidence that mainstream ASR systems underperform on atypical and disordered speech, we posit that widely used services (e.g., Google Speech-to-Text) exhibit reduced fairness for CLP speech, and we evaluate this claim empirically. To quantify fairness consistently, we introduce a simple fairness score (FS) that trades off overall error and between-group disparity. Despite formant disruptions, mild and moderate CLP speech retains partial spectro-temporal alignment with typical speech, motivating the use of mixing strategies to improve fairness. We systematically investigated severity-aware mixing of CLP and normal speech at different severity levels and assessed its effect on fairness. Three ASR models GMM-HMM, Whisper, and XLSR were evaluated on the AIISH (Kannada language) and NMCPC (English language) datasets. A mixing strategy that leverages severity-aware mixing of CLP and normal speech improves fairness on both English (NMCPC) and Kannada (AIISH) corpora. Notably, the word error rate (WER) decreased from 37.58% to 25.47% (GMM-HMM, AIISH) and from 35.74% to 21.72% (Whisper, NMCPC).

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