(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Aravinda, K.P.; Sandeepa, K.G.H.; Sedara, V. V.; Chamodya, A.K.Y.L.; Dharmasena, T.; Abeygunawardhana, P.K.W.
This paper proposes a digital preservation
solution for Sinhala audios to conserve those as documents
with noise reduction. The solution has implemented multiple
noise reduction techniques as a pre-processing step to remove
unwanted internal and external noises. A two-step, two-way
noise reduction process is applied to produce clean audios
based on Deep Convolutional Neural Network (DCNN) and
adaptive filter-based techniques. This approach implements
two separate noise reduction models for internal and external
noises. After that, the speech recognition decoder recognizes the
speech and converts it to a Unicode document by acoustic,
language, and pronunciation models using extracted audio
features from the denoised audio. Further, noise reduction
models are decoupled from the preservation solution and
exposed as a sub solution for multilingualism noise reduction,
supporting English and Sinhala audios.
