TITLE:
Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha
AUTHORS:
Tessfu Geteye Fantaye, Junqing Yu, Tulu Tilahun Hailu
KEYWORDS:
Automatic Speech Recognition, Multilingual DNN Modeling Methods, Basic Phone Acoustic Units, Rounded Phone Acoustic Units, Chaha
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.11 No.1,
January
9,
2020
ABSTRACT: Automatic speech recognition (ASR) is vital for very
low-resource languages for mitigating the extinction trouble. Chaha is one of
the low-resource languages, which suffers from the problem of resource
insufficiency and some of its phonological, morphological, and orthographic
features challenge the development and initiatives in the area of ASR. By
considering these challenges, this study is the first endeavor, which analyzed
the characteristics of the language, prepared speech corpus, and developed different
ASR systems. A small 3-hour read speech corpus was prepared and transcribed.
Different basic and rounded phone unit-based speech recognizers were explored
using multilingual deep neural network (DNN) modeling methods. The experimental
results demonstrated that all the basic phone and rounded phone unit-based
multilingual models outperformed the corresponding unilingual models with the
relative performance improvements of 5.47% to 19.87% and 5.74% to 16.77%,
respectively. The rounded phone unit-based multilingual models outperformed the
equivalent basic phone unit-based models with relative performance improvements
of 0.95% to 4.98%. Overall, we discovered that multilingual DNN modeling
methods are profoundly effective to develop Chaha speech recognizers. Both the
basic and rounded phone acoustic units are convenient to build Chaha ASR
system. However, the rounded phone unit-based models are superior in
performance and faster in recognition speed over the corresponding basic phone
unit-based models. Hence, the rounded phone units are the most suitable
acoustic units to develop Chaha ASR systems.