Detail výsledku
Resources and Benchmarks for Keyword Search in Spoken Audio From Low-Resource Indian Languages
Kesiraju Santosh, Ph.D., UPGM (FIT)
BANKA, R.
KETHIREDDY, R.
Gangashetty Suryakanth V, FIT (FIT)
This paper presents the resources and benchmarks developed for keyword search (KWS)in spoken audio from six low-resource Indian languages (from two families), namely Gujarati, Hindi,Marathi, Odia, Tamil, and Telugu. The current work on constructing keywords and building benchmarkKWS systems is inspired by the popular IARPA Babel program and the subsequent works on low-resourceKWS. The keywords are constructed by taking into account their properties i.e., occurrence, length, andaverage confusability; and their effects on the evaluation metric - the term-weighted value (TWV).We makeuse of freely available speech datasets, and reprocess them to create resources for KWS, thereby addingvalue to the existing speech resources. Four ASR-based KWS systems are built, and their performance isanalyzed across the three keyword properties on all the six languages. The prepared keywords and otherrelated resources to replicate our experiments are made available for the public.We believe that the analysisand guidelines provided in this paper will not only help the research community, but also practitioners andengineers to easily create KWS resources for newer languages, datasets, and scenarios.
Keyword search, low-resource languages, term-weighted value (TWV)
@article{BUT182528,
author="NADIMPALLI, V. and KESIRAJU, S. and BANKA, R. and KETHIREDDY, R. and GANGASHETTY, S.",
title="Resources and Benchmarks for Keyword Search in Spoken Audio From Low-Resource Indian Languages",
journal="IEEE Access",
year="2022",
volume="10",
number="2022",
pages="34789--34799",
doi="10.1109/ACCESS.2022.3162854",
issn="2169-3536",
url="https://ieeexplore.ieee.org/document/9743904"
}