Our research team is pushing the boundaries of what's possible with AI, developing innovative solutions for language understanding and generation.
Developing state-of-the-art ASR models with improved accuracy, lower latency, and support for more languages and dialects.
Researching cross-lingual transfer learning and zero-shot translation to improve performance on low-resource languages.
Building efficient, domain-specific language models optimized for Indic languages and enterprise use cases.
Advancing speaker identification and separation techniques for better multi-speaker audio processing.
Researching model compression, quantization, and optimization techniques for faster, more cost-effective AI deployment.
Developing federated learning and differential privacy methods to protect user data while maintaining model performance.
We present a novel approach to building efficient ASR models for low-resource languages using transfer learning and data augmentation techniques.
This paper explores effective strategies for transferring knowledge from high-resource to low-resource Indic languages in NLP tasks.
We introduce a lightweight neural architecture for real-time speaker diarization that achieves state-of-the-art accuracy with minimal latency.
We collaborate with leading universities and research institutions worldwide to advance the state of AI. Our partnerships enable us to tackle complex challenges and contribute to the global AI research community.
We're always open to collaborating with researchers and institutions. Let's work together to advance AI.