TY - GEN
T1 - Automatic Accent and Gender Recognition of Regional UK Speakers
AU - Jayne, C.
AU - Chang, V.
AU - Bailey, J.
AU - Xu, Q.A.
PY - 2022/6/15
Y1 - 2022/6/15
N2 - With the ubiquity of voice assistants across the UK and the world, speech recognition of the regional accents across the British Isles has proven challenging due to varying pronunciations. This paper proposes an automated recognition of the geographical origin and gender of a voice sample based on the six regional dialects of the United Kingdom. Twenty six features are extracted from 17,877 voice samples and then used to design, implement and evaluate machine learning classifiers based on Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Random Forest (RF) and k-nearest neighbors (k-NN) algorithms. The results suggest that the proposed approach could be applicable for areas such as e-commerce and the service industry, and it provides a contribution to NLP audio research.
AB - With the ubiquity of voice assistants across the UK and the world, speech recognition of the regional accents across the British Isles has proven challenging due to varying pronunciations. This paper proposes an automated recognition of the geographical origin and gender of a voice sample based on the six regional dialects of the United Kingdom. Twenty six features are extracted from 17,877 voice samples and then used to design, implement and evaluate machine learning classifiers based on Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Random Forest (RF) and k-nearest neighbors (k-NN) algorithms. The results suggest that the proposed approach could be applicable for areas such as e-commerce and the service industry, and it provides a contribution to NLP audio research.
KW - Accent classification
KW - Artificial neural networks
KW - Deep learning
KW - Speech recognition
UR - http://www.scopus.com/inward/record.url?scp=85133025279&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007/978-3-031-08223-8_6
U2 - 10.1007/978-3-031-08223-8_6
DO - 10.1007/978-3-031-08223-8_6
M3 - Conference publication
SN - 9783031082221
T3 - Communications in Computer and Information Science
SP - 67
EP - 80
BT - Engineering Applications of Neural Networks - 23rd International Conference, EAAAI/EANN 2022, Proceedings
A2 - Iliadis, Lazaros
A2 - Jayne, Chrisina
A2 - Tefas, Anastasios
A2 - Pimenidis, Elias
PB - Springer
ER -