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Development of dual stress-tolerant rice variety Jyothi with Saltoland Sub1genes through marker-assisted backcross breeding

Autor: John, Deepa, Rohini, P. C., Shylaraj, K. S.

Publikováno v: Cereal Research Communications; September 2024, Vol. 52 Issue: 3 p1119-1126, 8p

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Akademický článek

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Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR

Autor: Gupta, Abhishek, Parulekar, Amruta, Chattopadhyay, Sameep, Jyothi, Preethi

Automatic speech recognition (ASR) for low-resource languages remains a challenge due to the scarcity of labeled training data. Parameter-efficient fine-tuning and text-only adaptation are two popular methods that have been used to address such low-r

Externí odkaz: http://arxiv.org/abs/2410.13445

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SALSA: Speedy ASR-LLM Synchronous Aggregation

Autor: Mittal, Ashish, Prabhu, Darshan, Sarawagi, Sunita, Jyothi, Preethi

Harnessing pre-trained LLMs to improve ASR systems, particularly for low-resource languages, is now an emerging area of research. Existing methods range from using LLMs for ASR error correction to tightly coupled systems that replace the ASR decoder

Externí odkaz: http://arxiv.org/abs/2408.16542

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LoFTI: Localization and Factuality Transfer to Indian Locales

Autor: Simon, Sona Elza, Mondal, Soumen Kumar, Singhania, Abhishek, Sen, Sayambhu, Jyothi, Preethi

Large language models (LLMs) encode vast amounts of world knowledge acquired via training on large web-scale datasets crawled from the internet. However, these datasets typically exhibit a geographical bias towards English-speaking Western countries.

Externí odkaz: http://arxiv.org/abs/2407.11833

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Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data Selection

Autor: Fazili, Barah, Agrawal, Ashish Sunil, Jyothi, Preethi

Large language models (LLMs) are very proficient text generators. We leverage this capability of LLMs to generate task-specific data via zero-shot prompting and promote cross-lingual transfer for low-resource target languages. Given task-specific dat

Externí odkaz: http://arxiv.org/abs/2407.10582

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CharSS: Character-Level Transformer Model for Sanskrit Word Segmentation

Autor: Bhatt, Krishnakant, J, Karthika N, Ramakrishnan, Ganesh, Jyothi, Preethi

Subword tokens in Indian languages inherently carry meaning, and isolating them can enhance NLP tasks, making sub-word segmentation a crucial process. Segmenting Sanskrit and other Indian languages into subtokens is not straightforward, as it may inc

Externí odkaz: http://arxiv.org/abs/2407.06331

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Improving Self-supervised Pre-training using Accent-Specific Codebooks

Autor: Prabhu, Darshan, Gupta, Abhishek, Nitsure, Omkar, Jyothi, Preethi, Ganapathy, Sriram

Speech accents present a serious challenge to the performance of state-of-the-art end-to-end Automatic Speech Recognition (ASR) systems. Even with self-supervised learning and pre-training of ASR models, accent invariance is seldom achieved. In this

Externí odkaz: http://arxiv.org/abs/2407.03734

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