326 citations · 717 across the 90 of their papers we have counts for
18 papers · 1 filter
Pralekha: Cross-Lingual Document Alignment for Indic Languages
Sanjay Suryanarayanan, Haiyue Song, Mohammed Safi Ur Rahman Khan +2
Mining parallel document pairs for document-level machine translation (MT) remains challenging due to the limitations of existing Cross-Lingual Document Alignment (CLDA) techniques…
Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation
Praveen Srinivasa Varadhan, Amogh Gulati, Ashwin Sankar +8
Despite rapid advancements in TTS models, a consistent and robust human evaluation framework is still lacking. For example, MOS tests fail to differentiate between similar models,…
Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages
Ashwin Sankar, Sparsh Jain, Nikhil Narasimhan +6
Speech translation for Indian languages remains a challenging task due to the scarcity of large-scale, publicly available datasets that capture the linguistic diversity and domain…
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams
Srija Anand, Praveen Srinivasa Varadhan, Mehak Singal +1
Recent advancements in Text-to-Speech (TTS) technology have led to natural-sounding speech for English, primarily due to the availability of large-scale, high-quality web data. How…
IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS
Ashwin Sankar, Srija Anand, Praveen Srinivasa Varadhan +7
Recent advancements in text-to-speech (TTS) synthesis show that large-scale models trained with extensive web data produce highly natural-sounding output. However, such data is sca…
Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs
Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Dilip Venkatesh +3
Evaluating machine-generated text remains a significant challenge in NLP, especially for non-English languages. Current methodologies, including automated metrics, human assessment…
Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings
Praveen Srinivasa Varadhan, Ashwin Sankar, Giri Raju +1
We release Rasa, the first multilingual expressive TTS dataset for any Indian language, which contains 10 hours of neutral speech and 1-3 hours of expressive speech for each of the…
Empowering Low-Resource Language ASR via Large-Scale Pseudo Labeling
Kaushal Santosh Bhogale, Deovrat Mehendale, Niharika Parasa +4
In this study, we tackle the challenge of limited labeled data for low-resource languages in ASR, focusing on Hindi. Specifically, we explore pseudo-labeling, by proposing a generi…
LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR Systems
Tahir Javed, Janki Nawale, Sakshi Joshi +4
Hindi, one of the most spoken language of India, exhibits a diverse array of accents due to its usage among individuals from diverse linguistic origins. To enable a robust evaluati…
Enhancing Out-of-Vocabulary Performance of Indian TTS Systems for Practical Applications through Low-Effort Data Strategies
Srija Anand, Praveen Srinivasa Varadhan, Ashwin Sankar +2
Publicly available TTS datasets for low-resource languages like Hindi and Tamil typically contain 10-20 hours of data, leading to poor vocabulary coverage. This limitation becomes…
An Empirical Comparison of Vocabulary Expansion and Initialization Approaches for Language Models
Nandini Mundra, Aditya Nanda Kishore, Raj Dabre +3
Language Models (LMs) excel in natural language processing tasks for English but show reduced performance in most other languages. This problem is commonly tackled by continually p…
How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages?
Anushka Singh, Ananya B. Sai, Raj Dabre +3
While machine translation evaluation has been studied primarily for high-resource languages, there has been a recent interest in evaluation for low-resource languages due to the in…
Finding Blind Spots in Evaluator LLMs with Interpretable Checklists
Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Sshubam Verma +1
Large Language Models (LLMs) are increasingly relied upon to evaluate text outputs of other LLMs, thereby influencing leaderboards and development decisions. However, concerns pers…
IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages
Tahir Javed, Janki Atul Nawale, Eldho Ittan George +18
We present INDICVOICES, a dataset of natural and spontaneous speech containing a total of 7348 hours of read (9%), extempore (74%) and conversational (17%) audio from 16237 speaker…
IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages
Mohammed Safi Ur Rahman Khan, Priyam Mehta, Ananth Sankar +9
Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. O…
Airavata: Introducing Hindi Instruction-tuned LLM
Jay Gala, Thanmay Jayakumar, Jaavid Aktar Husain +8
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to…
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9
Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…
An Empirical Study of In-context Learning in LLMs for Machine Translation
Pranjal A. Chitale, Jay Gala, Raj Dabre
Recent interest has surged in employing Large Language Models (LLMs) for machine translation (MT) via in-context learning (ICL) (Vilar et al., 2023). Most prior studies primarily f…