Zobrazeno 1 - 10
of 24 702
pro vyhledávání: '"Jyothi ."'
Autor:
Varadaneshwari SK, Shilpa N Kugali, Deelip S Natekar, Reshma S, Basanagouda Patil, Mallikarjun ., Jayashree ., Jyothi ., Anjana ., Kiran .
Publikováno v:
RGUHS Journal of Nursing Sciences, Vol 13, Iss 2 (2023)
Background Dysmenorrhoea is a common gynaecological issue. It has been predominant among women in their late teenage and early twenties and normally declines with age. It likewise influences over 80 of ladies in their conceptive age.Method A cross-se
Externí odkaz:
https://doaj.org/article/d4a9d55f205349ecad62d714d3b6459c
Spontaneous or conversational multilingual speech presents many challenges for state-of-the-art automatic speech recognition (ASR) systems. In this work, we present a new technique AMPS that augments a multilingual multimodal ASR system with paraphra
Externí odkaz:
http://arxiv.org/abs/2411.18368
Routing and Spectrum Assignment (RSA) represents a significant challenge within Elastic Optical Networks (EONs), particularly in dynamic traffic scenarios where the network undergoes continuous changes. Integrating multiple modulation formats transfo
Externí odkaz:
http://arxiv.org/abs/2411.12442
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
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
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
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
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
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
Convolutions have become essential in state-of-the-art end-to-end Automatic Speech Recognition~(ASR) systems due to their efficient modelling of local context. Notably, its use in Conformers has led to superior performance compared to vanilla Transfo
Externí odkaz:
http://arxiv.org/abs/2407.03718