12 papers · 2 filters
Articulatory Source-Filter TTS: Physically Grounded Control through Vocal Tract Kinematics
Jesuraj Bandekar, Shinji Watanabe, Prasanta Kumar Ghosh
Modern neural text-to-speech (TTS) systems achieve remarkable acoustic fidelity but act as black boxes, offering little interpretable control over the vocal tract filter. We propos…
VAANI Noise Event Dataset: A curated spontaneous speech dataset annotated with timestamps for noise events
Pavan Kumar J, Agneedh Basu, Pranav Bhat +3
Most public sound-event corpora are optimized either for general audio tagging or for clean speech separation, and comparatively few provide strong timestamped noise annotations la…
SraVaani 1.0: Scaling Inclusive Speech Recognition for Indic Languages
Sujith Pulikodan, Agneedh Basu, Pavan Kumar J +4
India's linguistic landscape spans over 700 languages and thousands of dialects, yet the vast majority of automatic speech recognition (ASR) systems support only a small fraction o…
Enhancing Acoustic-to-Articulatory Inversion with Multi-Target Pretraining for Low-Resource Settings
Jesuraj Bandekar, Prasanta Kumar Ghosh
Acoustic-to-Articulatory Inversion (AAI) estimates vocal tract articulator movements from speech, benefiting tasks like ASR, speech synthesis, and speaker verification. While deep…
Audio--Image Alignment as a Continued-Pretraining Stage in Automatic Speech Recognition
Sujith Pulikodan, Nihar Desai, Prasanta Kumar Ghosh
Thousands of languages are spoken worldwide, yet many remain under-resourced for Automatic Speech Recognition (ASR) due to the limited availability of high-quality transcribed spee…
Vaani Benchmark V1.0: An Inclusive Multimodal Benchmark Dataset for Hindi
Sujith Pulikodan, Agneedh Basu, Saurabh Kumar +5
Benchmarking is critical for the systematic evaluation of machine learning systems. While several open-source datasets are available for Hindi, existing benchmarks remain limited i…
Analyzing Language and Geographical Variation in Speech Representations Across 60 Indic Languages
Pavan Kumar J, Agneedh Basu, Pranav Bhat +4
Self-supervised speech encoders are often fine-tuned with language supervision, which can overlook geographical variation. To understand the learned representations under joint sup…
An Analysis of the Effectiveness of Synthetic Speech Data for ASR Fine-tuning in Selected Indic Languages
Sujith Pulikodan, Agneedh Basu, Pavan Kumar +4
Synthetic data has the potential to be a valuable resource for training machine learning models, particularly Automatic Speech Recognition (ASR) Systems; however, its effectiveness…
Factors affecting ASR performance: A study using state of the art ASR models in Indic Languages
Agneedh Basu, Pavan Kumar J, Pranav Bhat +4
ASR performance varies across languages, speakers, and recording conditions, yet systematic analysis for Indic languages remain limited. We present a large-scale study of decoded o…
A Comparative Study of Pre-trained Speech Encoders and Training Objectives for Large-Scale Indic Spoken Language Identification
Agneedh Basu, Pavan Kumar J, Sujith P +3
Spoken language identification (LID) for Indian languages is a challenging problem due to the large number of languages, significant phonetic overlap among related varieties, and t…
Bottleneck Transformer-Based Approach for Improved Automatic STOI Score Prediction
Amartyaveer, Murali Kadambi, Chandra Mohan Sharma +2
In this study, we have presented a novel approach to predict the Short-Time Objective Intelligibility (STOI) metric using a bottleneck transformer architecture. Traditional methods…
VAANI: Capturing the language landscape for an inclusive digital India
Sujith Pulikodan, Abhayjeet Singh, Agneedh Basu +17
Voice based technologies have the potential to bridge digital accessibility gaps; however, existing datasets fail to capture the linguistic and regional diversity of Indic language…