AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition

Afroz Ahamad, Ankit Anand, Pranesh Bhargava


Abstract
Modern Automatic Speech Recognition (ASR) technology has evolved to identify the speech spoken by native speakers of a language very well. However, identification of the speech spoken by non-native speakers continues to be a major challenge for it. In this work, we first spell out the key requirements for creating a well-curated database of speech samples in non-native accents for training and testing robust ASR systems. We then introduce AccentDB, one such database that contains samples of 4 Indian-English accents collected by us, and a compilation of samples from 4 native-English, and a metropolitan Indian-English accent. We also present an analysis on separability of the collected accent data. Further, we present several accent classification models and evaluate them thoroughly against human-labelled accent classes. We test the generalization of our classifier models in a variety of setups of seen and unseen data. Finally, we introduce accent neutralization of non-native accents to native accents using autoencoder models with task-specific architectures. Thus, our work aims to aid ASR systems at every stage of development with a database for training, classification models for feature augmentation, and neutralization systems for acoustic transformations of non-native accents of English.
Anthology ID:
2020.lrec-1.659
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5351–5358
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.659
DOI:
Bibkey:
Cite (ACL):
Afroz Ahamad, Ankit Anand, and Pranesh Bhargava. 2020. AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5351–5358, Marseille, France. European Language Resources Association.
Cite (Informal):
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition (Ahamad et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.659.pdf
Data
AccentDB