5 citations · 5 across the 3 of their papers we have counts for
5 papers
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…
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…
Discovering phoneme-specific critical articulators through a data-driven approach
Jesuraj Bandekar, Sathvik Udupa, Prasanta Kumar Ghosh
We propose an approach for learning critical articulators for phonemes through a machine learning approach. We formulate the learning with three models trained end to end. First, w…
Speaking rate attention-based duration prediction for speed control TTS
Jesuraj Bandekar, Sathvik Udupa, Abhayjeet Singh +6
With the advent of high-quality speech synthesis, there is a lot of interest in controlling various prosodic attributes of speech. Speaking rate is an essential attribute towards m…
Model Adaptation for ASR in low-resource Indian Languages
Abhayjeet Singh, Arjun Singh Mehta, Ashish Khuraishi K S +17
Automatic speech recognition (ASR) performance has improved drastically in recent years, mainly enabled by self-supervised learning (SSL) based acoustic models such as wav2vec2 and…