Transcription of Telugu TV news using ASR

Reddy, M. Ram; Laxminarayana, P.; Ramana, A. V.; Markandeya, J L; Bhaskar, Jasmine; Harish, B.; Jagadheesh, S.; Sumalatha, Edapalli

doi:10.1109/icacci.2015.7275832

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…Transcriptions usually follow a five-stage workflow in processing the speech files into text (Moore & Llompart, 2017). This is a pipeline that can be tried in a proof-of-concept system for a low resource language, together with associated toolkits such as CMU Sphinx to create and test an ASR system (Pantazoglou et al, 2018;Reddy et al, 2015).…”

Section: Proof Of Conceptmentioning

confidence: 99%

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Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

Wanjawa

Wanzare

Indede

et al. 2023

JLCL

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Indigenous African languages are categorized as under-served in Natural Language Processing. They therefore experience poor digital inclusivity and information access. The processing challenge with such languages has been how to use machine learning and deep learning models without the requisite data. The Kencorpus project intends to bridge this gap by collecting and storing text and speech data that is good enough for data-driven solutions in applications such as machine translation, question answering and transcription in multilingual communities. The Kencorpus dataset is a text and speech corpus for three languages predominantly spoken in Kenya: Swahili, Dholuo and Luhya (three dialects of Lumarachi, Lulogooli and Lubukusu). Data collection was done by researchers who were deployed to the various data collection sources such as communities, schools, media, and publishers. The Kencorpus' dataset has a collection of 5,594 items, being 4,442 texts of 5.6 million words and 1,152 speech files worth 177 hours. Based on this data, other datasets were also developed such as Part of Speech tagging sets for Dholuo and the Luhya dialects of 50,000 and 93,000 words tagged respectively. We developed 7,537 Question-Answer pairs from 1,445 Swahili texts and also created a text translation set of 13,400 sentences from Dholuo and Luhya into Swahili. The datasets are useful for downstream machine learning tasks such as model training and translation. Additionally, we developed two proof of concept systems: for Kiswahili speech-to-text and a machine learning system for Question Answering task. These proofs provided results of a performance of 18.87% word error rate for the former, and 80% Exact Match (EM) for the latter system. These initial results give great promise to the usability of Kencorpus to the machine learning community. Kencorpus is one of few public domain corpora for these three low resource languages and forms a basis of learning and sharing experiences for similar works especially for low resource languages. Challenges in developing the corpus included deficiencies in the data sources, data cleaning challenges, relatively short project timelines and the Coronavirus disease (COVID-19) pandemic that restricted movement and hence the ability to get the data in a timely manner.

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Section: Proof Of Conceptmentioning

confidence: 99%

“…This created a Kiswahili phoneme dictionary of 31,759 word-phoneme pairs (Awino et al, 2022). The SST model was based on CMU Sphinx STT toolkit (Pantazoglou et al, 2018;Reddy et al, 2015).…”

Section: Kencorpus Proof Of Conceptmentioning

confidence: 99%

Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

Wanjawa

Wanzare

Indede

et al. 2023

JLCL

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COVID-19 Social Distancing with Speech-Enabled Online Market Place for Farmers

Mohan

Sheela

Sudhakar

2021

Lecture Notes in Networks and Systems

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has been rampant in most countries across the world, leading to a global crisis, and it has been so dangerous because of its ability to spread through any medium especially when there is physical contact of any object. So, this puts many people including the farmers and the customers at a bigger risk when they sell or buy products physically in a market place. This is a major challenge to an Indian farmer today to sell the product at the right price at the right time. Moreover, social distancing is the need of the hour, and hence, it is an even bigger challenge to monitor the market place to avoid physical contact. Also, many initiatives by governments, local bodies, and cooperative societies to eliminate the intermediaries are not turning effective due to the non-awareness, non-accessibility, ignorance, and ease of usage difficulties. Keeping the current pandemic situation in mind and social distancing being the need of the hour, this paper proposes a speech-enabled interactive voice response (SEIVR) wherein the farmer and the customer can sell and buy products online, i.e., without any physical contact. In the current technology era, online market platforms like Amazon, Flipkart, and Olx are effectively cutting short these supply-chain overheads by establishing a direct connection between buyer and seller. Hence, the proposed implementation of a speech-enabled interactive voice response (SEIVR)-based online market place for farmers which even ensure social distancing will help to reduce the spread of the virus. The objective of this work is to build an agriculture-based mobile application with speech-enabled interactive voice response (SEIVR) based on speech recognition application with the Telugu language as a case study.

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Robust automatic continuous speech recognition for 'Adi', a zero-resource indigenous language of Arunachal Pradesh

Sasmal

Saring

2022

Sādhanā

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Transcription of Telugu TV news using ASR

Cited by 7 publications

References 3 publications

Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

COVID-19 Social Distancing with Speech-Enabled Online Market Place for Farmers

Robust automatic continuous speech recognition for 'Adi', a zero-resource indigenous language of Arunachal Pradesh

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