Version 1
: Received: 12 February 2024 / Approved: 13 February 2024 / Online: 13 February 2024 (14:26:24 CET)
How to cite:
Ejigu, Y.A.; Asfaw, T.T. Enhancing Amharic Speech Recognition in Noisy Conditions through End-to-End Deep Learning. Preprints2024, 2024020754. https://doi.org/10.20944/preprints202402.0754.v1
Ejigu, Y.A.; Asfaw, T.T. Enhancing Amharic Speech Recognition in Noisy Conditions through End-to-End Deep Learning. Preprints 2024, 2024020754. https://doi.org/10.20944/preprints202402.0754.v1
Ejigu, Y.A.; Asfaw, T.T. Enhancing Amharic Speech Recognition in Noisy Conditions through End-to-End Deep Learning. Preprints2024, 2024020754. https://doi.org/10.20944/preprints202402.0754.v1
APA Style
Ejigu, Y.A., & Asfaw, T.T. (2024). Enhancing Amharic Speech Recognition in Noisy Conditions through End-to-End Deep Learning. Preprints. https://doi.org/10.20944/preprints202402.0754.v1
Chicago/Turabian Style
Ejigu, Y.A. and Tesfa Tegegne Asfaw. 2024 "Enhancing Amharic Speech Recognition in Noisy Conditions through End-to-End Deep Learning" Preprints. https://doi.org/10.20944/preprints202402.0754.v1
Abstract
Speech recognition, also known as automatic speech recognition (ASR), is a technology that enables software to transcribe spoken language into text. However, existing Amharic ASR methods require multiple separate blocks, such as language, acoustic, and pronunciation models with dictionaries, which can be time-consuming and influence performance. This study proposes an approach that replaces much of the speech pipeline with a single recurrent neural network (RNN) architecture. Our proposed architecture is based on a hybrid approach that combines a convolutional neural network (CNN) with a recurrent neural network (RNN) and a connectionist temporal classification (CTC) loss function. We conducted several experiments with noisy audio data that contain 20,000 valid sentences. The model was evaluated using the word error rate (WER) metric, achieving impressive results of 7% WER on noisy data. This approach has significant implications for the field of speech recognition, as it reduces the human effort required to create dictionaries and improves the efficiency and accuracy of ASR systems, making them more practical for real-world applications.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
