Multilingual Spoken Words Corpus

Mark Mazumder, Sharad Chitlangia, Colby Banbury, Yiping Kang, Juan Manuel Ciro, Keith Achorn, Daniel Galvez, Mark Sabini, Peter Mattson, David Kanter, Greg Diamos, Pete Warden, Josh Meyer, Vijay Janapa Reddi

Keywords: keyword spotting, speech recognition, low resource languages

Abstract: Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages collectively spoken by over 5 billion people, for academic research and commercial applications in keyword spotting and spoken term search, licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, totaling 23.4 million 1-second spoken examples (over 6,000 hours). The dataset has many use cases, ranging from voice-enabled consumer devices to call center automation. We generate this dataset by applying forced alignment on crowd-sourced sentence-level audio to produce per-word timing estimates for extraction. All alignments are included in the dataset. We provide a detailed analysis of the contents of the data and contribute methods for detecting potential outliers. We report baseline accuracy metrics on keyword spotting models trained from our dataset compared to models trained on a manually-recorded keyword dataset. We conclude with our plans for dataset maintenance, updates, and open-sourced code.

TL;DR: Multilingual Spoken Words Corpus is a speech dataset of over 340,000 spoken words in 50 languages, with over 23.4 million examples.

URL: https://mlcommons.org/en/multilingual-spoken-words (the dataset will be available during and after the NeurIPS 2021 conference)