Abstract

Automatic stuttering detection is hindered by two major challenges: extreme class imbalance, where minority stutter types represent less than five percent of the data, and long-range temporal dependencies that span multiple non-adjacent speech segments. We address these issues with HyDRA, a hierarchical hypergraph neural network that factorizes the detection task into binary disfluency recognition followed by subtype classification. HyDRA constructs view-specific hypergraphs from wav2vec2 and HuBERT embeddings, where hyperedges connect multiple acoustically similar segments, enabling modeling of repetitive and prosodic patterns that pairwise graphs cannot represent. Experiments on the SEP-28k dataset show that HyDRA achieves 47.2 macro F1 for subtype classification, compared to 31.4 for flat baselines, with gains exceeding 16 points on minority classes. Cross-dataset evaluation on FluencyBank confirms generalization across domains. These results demonstrate that hierarchical factorization, higher-order hypergraph modeling, and structured multi-view integration provide a principled solution to the imbalance and dependency challenges in automatic stuttering detection.

Abstract

Despite Telugu being spoken by over 80 million
people, speech translation research for this morphologically rich language remains severely
underexplored. We address this gap by developing a high-quality Telugu-English speech
translation benchmark from 46 hours of manually verified CSTD corpus data (30h/8h/8h
train/dev/test split). Our systematic comparison of cascaded versus end-to-end architectures shows that while IndicWhisper + IndicMT achieves the highest performance due
to extensive Telugu-specific training data, finetuned SeamlessM4T models demonstrate remarkable competitiveness despite using significantly less Telugu-specific training data.
This finding suggests that with careful hyperparameter tuning and sufficient parallel data
(potentially less than 100 hours), end-to-end
systems can achieve performance comparable
to cascaded approaches in low-resource settings. Our metric reliability study evaluating
BLEU, METEOR, ChrF++, ROUGE-L, TER,
and BERTScore against human judgments reveals that traditional metrics provide better
quality discrimination than BERTScore for
Telugu-English translation. The work delivers
three key contributions: a reproducible Telugu-
English benchmark, empirical evidence of competitive end-to-end performance potential in
low-resource scenarios, and practical guidance
for automatic evaluation in morphologically
complex language pairs.

Abstract

Automatic speech recognition for low-resource
languages remains fundamentally constrained
by the scarcity of labeled data and compu-
tational resources required by state-of-the-art
models. We present a systematic investigation
into cross-lingual continuous pretraining for
low-resource languages, using Perso-Arabic
languages (Persian, Arabic, and Urdu) as our
primary case study. Our approach demon-
strates that strategic utilization of unlabeled
speech data can effectively bridge the resource
gap without sacrificing recognition accuracy.
We construct a 3,000-hour multilingual corpus
through a scalable unlabeled data collection
pipeline and employ targeted continual pretrain-
ing combined with morphologically-aware tok-
enization to develop a 300M parameter model
that achieves performance comparable to sys-
tems 5 times larger. Our model outperforms
Whisper Large v3 (1.5B parameters) on Persian
and achieves competitive results on Arabic and
Urdu despite using significantly fewer parame-
ters and substantially less labeled data. These
findings challenge the prevailing assumption
that ASR quality scales primarily with model
size, revealing instead that data relevance and
strategic pretraining are more critical factors for
low-resource scenarios. This work provides a
practical pathway toward inclusive speech tech-
nology, enabling effective ASR for underrepre-
sented languages without dependence on mas-
sive computational infrastructure or proprietary
datasets.

Abstract

Educational technologies like reading tutors and assistive applications rely on accurate pronunciation modeling. Low resource Perso-Arabic languages such as Persian and Urdu lack sufficient Grapheme-to-Phoneme resources compared to Arabic, limiting use in educational tools.
We address this by exploiting phonological similarities between Arabic, Persian, and Urdu to construct a unified Common Phone Space (CPS-APU) that enables cross-lingual knowledge transfer. Using linguistic expertise, we develop a shared phoneme mapping across the three languages.
To evaluate the hypothesis that lower-resource languages benefit from such shared representations, we test and enrich our G2P parser across three stages: (A) expert-authored rules, (B) corpus-mined rules from alignments, and (C) LLM-powered generation using lightweight instruction-tuned models. CPS
framework consistently outperforms monolingual approaches for low-resource languages, with the LLM- augmented stage yielding the best results.

Abstract

Stuttering is a complex speech disorder that challenges both ASR systems and clinical assessment. We propose a multimodal stuttering detection and classification model that integrates acoustic and linguistic features through a two-stage fusion mechanism. Fine-tuned Wav2Vec 2.0 and HuBERT extract acoustic embeddings, which are early fused with MFCC features to capture fine-grained spectral and phonetic variations, while Llama-2 embeddings from Whisper ASR transcriptions provide linguistic context. To enhance robustness against out-of-distribution speech patterns, we incorporate Retrieval-Augmented Generation or adaptive classification. Our model achieves state-of-the-art performance on SEP-28k and FluencyBank, demonstrating significant improvements in detecting challenging stuttering events. Additionally, our analysis highlights the complementary nature of acoustic and linguistic modalities, reinforcing the need for multimodal approaches in speech disorder detection.

Abstract

Abstract- Data is useful information collected, programmed, generated and/or altered for a specific application/s or for future use. Such data sampling/data record is of utmost importance in single use airborne systems especially the military warfare. The operator will be interested in acquisition of high speed data during flight but will be denied such opportunity due to protocol and bandwidth restrictions therein.
This paper deals with one such data network which is popular with many avionics designers, the conventional command response protocol MIL-STD- 1553B which restricts the overall baud rate to 1Mbps. This paper proposes a unique data fusion technique to combine (i) a 100x baud rate Multi Carrier Wave (MCW) with (ii) conventional baud rate of 1Mbps on standard MIL-STD-1553B hardware. OFDM (Orthogonal Frequency Division Multiplexing) technique] and bus protocol that is used to achieve the data flow and fusion are explained in detail.

Abstract

Traditionally, dysarthric speech intelligibility assessment systems have focused on speech as the primary input, utilizing methods such as extraction of relevant speech features, classification models, alignment of Automatic Speech Recognition (ASR) outputs, and comparisons between speech representations of dysarthric and healthy speakers. However, to achieve an automated intelligibility assessment that closely mirrors the auditory-perceptual evaluations conducted by clinicians, a model that captures both the acoustic characteristics of dysarthric speech and the linguistic structure related to word pronunciation are needed. Inspired by the practices of clinicians, this study introduces a novel text-guided dysarthric speech intelligibility assessment framework that leverages custom keyword spotting (DySIA-CKWS). The model evaluates intelligibility by detecting specific keywords and is extensively tested using UA-Speech database for speaker-wise analysis and across word groups of varying complexity. To ensure robustness, the system's performance is further validated on TORGO database, demonstrating its adaptability in cross-database settings. Statistical analysis demonstrates strong alignment between predicted and subjective intelligibility scores, with a Pearson Correlation Coefficient (PCC) of 0.9588 and a Spearman's Correlation Coefficient (SCC) of 0.9141, achieved using the proposed system on the UA-Speech database. The findings emphasize the importance of word selection and showcase the model's effectiveness in diagnosing dysarthric speech, offering a significant advancement in intelligibility assessment methodologies.

Abstract

This paper investigates the nuanced distinctions between typical and atypical speech disfluencies, focusing on features captured by self-supervised models. Typical disfluencies are natural, non-pathological irregularities in speech, while atypical disfluencies are linked to speech disorders like stuttering, characterized by more frequent and severe disruptions. Despite progress in automatic disfluency detection, limited research addresses the direct classification of these two types. This study leverages intermediate representations from four pretrained models Wav2Vec2.0, HuBERT, WavLM, and TERA, to analyze and classify typical and stuttered disfluencies. The experiments utilize two novel Indian English datasets, IIITH-IEDE and IIITH-TISA, enabling a comprehensive analysis of disfluency patterns in a linguistically diverse context. Classification experiments with support vector machines (SVM) and convolutional neural networks (CNN) reveal that features from HuBERT's 5th layer--balancing low-level acoustic and high-level semantic information--achieve a peak F1 score of 0.97. These findings highlight the importance of intermediate layer representations of self-supervised models in distinguishing nuanced speech variations and contribute to robust and interpretable systems for speech disfluency classification.

Abstract

Dysarthric speech poses significant challenges in developing as-
sistive technologies, primarily due to the limited availability of
data. Recent advances in neural speech synthesis, especially
zero-shot voice cloning, facilitate synthetic speech generation
for data augmentation; however, they may introduce biases to-
wards dysarthric speech. In this paper, we investigate the effec-
tiveness of state-of-the-art F5-TTS in cloning dysarthric speech
using TORGO dataset, focusing on intelligibility, speaker sim-
ilarity, and prosody preservation. We also analyze potential
biases using fairness metrics like Disparate Impact and Parity
Difference to assess disparities across dysarthric severity lev-
els. Results show that F5-TTS exhibits a strong bias toward
speech intelligibility over speaker and prosody preservation in
dysarthric speech synthesis. Insights from this study can help
integrate fairness-aware dysarthric speech synthesis, fostering
the advancement of more inclusive speech technologies.

Abstract

Automatic detection and classification of stuttering events remain a challenging problem in speech processing, particularly due to the variability of disfluency
types and the scarcity of large annotated datasets. This paper presents a signal processing-based approach for stuttering classification that avoids the data-intensive requirements of machine learning models. Our method focuses on syllable-level analysis using key acoustic parameters such as energy patterns, pitch contours, spectral stability, and temporal features, implemented through rule-based classification with predefined thresholds. The methodology involves automatic syllable segmentation followed by rule-based classification using thresholds on acoustic features. The dataset comprises annotated read and spontaneous speech from 106 Kannada-speaking adults, including 26 new participants evaluated during clinical trials. Three certified Speech-Language Pathologists (SLPs) conducted perceptual evaluations, and system outputs were validated using intraclass correlation (ICC), yielding strong agreement (textgreater 0.82) across stuttering types. The system achieved classification accuracies of 89% for blocks, 83% for repetitions, and 81% for prolongations. However, the system has several limitations: it relies on static threshold values, exhibits sensitivity to noise, and lacks automated severity grading. Additionally, generalizability across languages and speaking conditions requires further exploration. Machine learning models such as SVM, TDNN, and LSTM were trained and achieved a performance of around 30 to 60% accuracy. Our approach offers high interpretability, low computational cost, and real-time feasibility compared to machine learning methods. Future work will focus on adaptive thresholding, noise-robust processing, and hybrid approaches integrating signal processing with lightweight learning-based refinement.