2 citations · 2 across the 6 of their papers we have counts for
6 papers
Phoneme- and Word-Level Metrics Using Self-Supervised Speech Representations for Forced Alignment Evaluation
V. S. D. S. Mahesh Akavarapu, Michael Daniel, Gerhard Jäger
Forced alignment evaluation typically requires manually annotated timestamps, limiting large-scale and multilingual analysis. We introduce two corpus-level metrics based on self-su…
Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages
V. S. D. S. Mahesh Akavarapu, Michael Daniel, Gerhard Jäger
We present a phoneme-level analysis of automatic speech recognition (ASR) for two low-resourced and phonologically complex East Caucasian languages, Archi and Rutul, based on curat…
A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs
V. S. D. S. Mahesh Akavarapu, Hrishikesh Terdalkar, Pramit Bhattacharyya +5
Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across diverse tasks and languages. In this study, we focus on natural language understanding…
A Likelihood Ratio Test of Genetic Relationship among Languages
V. S. D. S. Mahesh Akavarapu, Arnab Bhattacharya
Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However,…
Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer
V. S. D. S. Mahesh Akavarapu, Arnab Bhattacharya
Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tas…
Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction
V. S. D. S. Mahesh Akavarapu, Arnab Bhattacharya
Phonological reconstruction is one of the central problems in historical linguistics where a proto-word of an ancestral language is determined from the observed cognate words of da…