8 papers · 1 filter
Vimarsha: Faithful ASR Evaluation for Indian Languages with Demographic Diversity, In-the-Wild Audio and Spelling Variations
Kaushal Santosh Bhogale, Srija Anand, Sadakopa Ramakrishnan Thothathiri +3
Evaluation benchmarks for Indian language automatic speech recognition (ASR) suffer from two systematic biases: optimistic scores from clean, controlled audio conditions, and pessi…
Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages
Srija Anand, Ashwin Sankar, Ishvinder Sethi +10
Crowdsourced pairwise evaluation has emerged as a scalable approach for assessing foundation models. However, applying it to Text to Speech(TTS) introduces high variance due to lin…
Afri-MCQA: Multimodal Cultural Question Answering for African Languages
Atnafu Lambebo Tonja, Srija Anand, Emilio Villa-Cueva +16
Africa is home to over one-third of the world's languages, yet remains underrepresented in AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering b…
Phir Hera Fairy: An English Fairytaler is a Strong Faker of Fluent Speech in Low-Resource Indian Languages
Praveen Srinivasa Varadhan, Srija Anand, Soma Siddhartha +1
What happens when an English Fairytaler is fine-tuned on Indian languages? We evaluate how the English F5-TTS model adapts to 11 Indian languages, measuring polyglot fluency, voice…
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,…
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…
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…