MunTTS: A Text-to-Speech System for Mundari

no code implementations • 28 Jan 2024 • Varun Gumma, Rishav Hada, Aditya Yadavalli, Pamir Gogoi, Ishani Mondal, Vivek Seshadri, Kalika Bali

We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family.

Speech Synthesis

X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents

1 code implementation • 30 Jun 2023 • Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gaël de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina J. Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica S. Lam

We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.

Entity Alignment Machine Translation +1

MinUn: Accurate ML Inference on Microcontrollers

no code implementations • 29 Oct 2022 • Shikhar Jaiswal, Rahul Kiran Kranti Goli, Aayan Kumar, Vivek Seshadri, Rahul Sharma

Running machine learning inference on tiny devices, known as TinyML, is an emerging research area.

Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi

no code implementations • 26 Jun 2022 • Ritesh Kumar, Siddharth Singh, Shyam Ratan, Mohit Raj, Sonal Sinha, Bornini Lahiri, Vivek Seshadri, Kalika Bali, Atul Kr. Ojha

In this paper we discuss an in-progress work on the development of a speech corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and Magahi using the field methods of linguistic data collection.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications

no code implementations • 8 Jul 2021 • Nikhil Pratap Ghanathe, Vivek Seshadri, Rahul Sharma, Steve Wilton, Aayan Kumar

Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices.

BIG-bench Machine Learning

Multilingual and code-switching ASR challenges for low resource Indian languages

1 code implementation • 1 Apr 2021 • Anuj Diwan, Rakesh Vaideeswaran, Sanket Shah, Ankita Singh, Srinivasa Raghavan, Shreya Khare, Vinit Unni, Saurabh Vyas, Akash Rajpuria, Chiranjeevi Yarra, Ashish Mittal, Prasanta Kumar Ghosh, Preethi Jyothi, Kalika Bali, Vivek Seshadri, Sunayana Sitaram, Samarth Bharadwaj, Jai Nanavati, Raoul Nanavati, Karthik Sankaranarayanan, Tejaswi Seeram, Basil Abraham

For this purpose, we provide a total of ~600 hours of transcribed speech data, comprising train and test sets, in these languages including two code-switched language pairs, Hindi-English and Bengali-English.

Automatic Speech Recognition (ASR) Sentence

Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers

no code implementations • LREC 2020 • Basil Abraham, Danish Goel, Divya Siddarth, Kalika Bali, Manu Chopra, Monojit Choudhury, Pratik Joshi, Preethi Jyoti, Sunayana Sitaram, Vivek Seshadri

Unfortunately, collecting labelled speech data in any language is an expensive and resource-intensive task.

GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users

1 code implementation • Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST'19) 2019 • Shishir G. Patil, Don Dennis, Chirag Pabbaraju, Nadeem Shaheer, Harsha Vardhan Simhadri, Vivek Seshadri, Manik Varma, Prateek Jain

Our in-lab study shows that GesturePod achieves 92% gesture recognition accuracy and can help perform common smartphone tasks faster.

Gesture Recognition Time Series Analysis +1

PipeDream: Fast and Efficient Pipeline Parallel DNN Training

1 code implementation • 8 Jun 2018 • Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil Devanur, Greg Ganger, Phil Gibbons

PipeDream is a Deep Neural Network(DNN) training system for GPUs that parallelizes computation by pipelining execution across multiple machines.

Distributed, Parallel, and Cluster Computing