Brij Mohan Lal Srivastava


2022

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Keynote Speech - Voice anonymization and the GDPR
Brij Mohan Lal Srivastava
Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference

2020

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Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi
Devansh Mehta | Sebastin Santy | Ramaravind Kommiya Mothilal | Brij Mohan Lal Srivastava | Alok Sharma | Anurag Shukla | Vishnu Prasad | Venkanna U | Amit Sharma | Kalika Bali
Proceedings of the Twelfth Language Resources and Evaluation Conference

The primary obstacle to developing technologies for low-resource languages is the lack of usable data. In this paper, we report the adaption and deployment of 4 technology-driven methods of data collection for Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. In the process of data collection, we also help in its revival by expanding access to information in Gondi through the creation of linguistic resources that can be used by the community, such as a dictionary, children’s stories, an app with Gondi content from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform. At the end of these interventions, we collected a little less than 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. The larger goal of the project is collecting enough data in Gondi to build and deploy viable language technologies like machine translation and speech to text systems that can help take the language onto the internet.

2018

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Too Many Questions? What Can We Do? : Multiple Question Span Detection
Prathyusha Danda | Brij Mohan Lal Srivastava | Manish Shrivastava
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Phone Merging For Code-Switched Speech Recognition
Sunit Sivasankaran | Brij Mohan Lal Srivastava | Sunayana Sitaram | Kalika Bali | Monojit Choudhury
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching

Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of code-switched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2% WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.

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Phonetically Balanced Code-Mixed Speech Corpus for Hindi-English Automatic Speech Recognition
Ayushi Pandey | Brij Mohan Lal Srivastava | Rohit Kumar | Bhanu Teja Nellore | Kasi Sai Teja | Suryakanth V. Gangashetty
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Towards developing a phonetically balanced code-mixed speech corpus for Hindi-English ASR
Ayushi Pandey | Brij Mohan Lal Srivastava | Suryakanth Gangashetty
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

2016

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Vaidya: A Spoken Dialog System for Health Domain
Prathyusha Danda | Brij Mohan Lal Srivastava | Manish Shrivastava
Proceedings of the 13th International Conference on Natural Language Processing