Tusom2021: A Phonetically Transcribed Speech Dataset from an Endangered Language for Universal Phone Recognition Experiments

by   David R. Mortensen, et al.

There is growing interest in ASR systems that can recognize phones in a language-independent fashion. There is additionally interest in building language technologies for low-resource and endangered languages. However, there is a paucity of realistic data that can be used to test such systems and technologies. This paper presents a publicly available, phonetically transcribed corpus of 2255 utterances (words and short phrases) in the endangered Tangkhulic language East Tusom (no ISO 639-3 code), a Tibeto-Burman language variety spoken mostly in India. Because the dataset is transcribed in terms of phones, rather than phonemes, it is a better match for universal phone recognition systems than many larger (phonemically transcribed) datasets. This paper describes the dataset and the methodology used to produce it. It further presents basic benchmarks of state-of-the-art universal phone recognition systems on the dataset as baselines for future experiments.


page 1

page 2

page 3

page 4


Universal Phone Recognition with a Multilingual Allophone System

Multilingual models can improve language processing, particularly for lo...

Differentiable Allophone Graphs for Language-Universal Speech Recognition

Building language-universal speech recognition systems entails producing...

Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

This paper explores the difficulties of annotating transcribed spoken Du...

Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties

Models pre-trained on multiple languages have shown significant promise ...

Cross-lingual and Multilingual Spoken Term Detection for Low-Resource Indian Languages

Spoken Term Detection (STD) is the task of searching for words or phrase...

Phone-aware Neural Language Identification

Pure acoustic neural models, particularly the LSTM-RNN model, have shown...

Common Phone: A Multilingual Dataset for Robust Acoustic Modelling

Current state of the art acoustic models can easily comprise more than 1...