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:
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.lrec-1.659.pdf
- Data
- AccentDB