An examination of neural network approaches to speech recognition in Igbo language
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- NGN 5000
Background of the Study
Neural network models have revolutionized the field of speech recognition by enabling systems to learn complex patterns from large datasets. In the context of the Igbo language, these approaches are being increasingly applied to develop automated speech recognition systems that accurately capture the nuances of spoken Igbo. Recent advances in deep learning have led to the creation of end-to-end neural models that handle acoustic variability and dialectal differences effectively (Ibe, 2023). Such models are trained on extensive datasets that include diverse speech samples, allowing them to identify subtle phonetic features and tonal inflections characteristic of Igbo (Okoye, 2024). The application of neural networks in Igbo speech recognition not only supports language documentation and transcription efforts but also enhances accessibility in educational and administrative settings. Moreover, the integration of recurrent neural networks and convolutional layers has improved the robustness of these systems against background noise and variable speech patterns (Nduka, 2025). This study investigates the effectiveness of neural network approaches in recognizing Igbo speech, focusing on model performance, challenges in data variability, and strategies for further optimization to support the preservation and propagation of the Igbo language.
Statement of the Problem
Despite significant progress in neural network-based speech recognition, current models for Igbo often struggle with dialectal variations, rapid speech, and background noise, leading to suboptimal transcription accuracy (Ibe, 2023; Okoye, 2024). The limited availability of high-quality, annotated Igbo speech corpora further constrains model training and evaluation. These challenges hinder the development of reliable automatic speech recognition systems for Igbo, which are crucial for language documentation and accessibility. Thus, there is a need for a detailed examination of existing neural network approaches to identify their limitations and recommend improvements tailored to the specific characteristics of Igbo speech.
Objectives of the Study
- To evaluate the performance of current neural network models in Igbo speech recognition.
- To identify challenges posed by dialectal and acoustic variability in Igbo.
- To recommend improvements for enhancing neural network approaches for Igbo speech recognition.
Research Questions
- How effective are neural network models in recognizing Igbo speech?
- What challenges do dialectal variations and acoustic variability present?
- How can neural network models be optimized for better Igbo speech recognition?
Significance of the Study
This study is significant as it examines advanced neural network approaches in Igbo speech recognition, contributing to improved language technology and accessibility. Enhanced recognition models will benefit language documentation, educational tools, and digital communication, supporting the broader goal of preserving Igbo linguistic heritage. The findings will inform future research and practical applications in automated speech processing.
Scope and Limitations of the Study
This study focuses on neural network approaches to speech recognition for the Igbo language and does not include other speech processing techniques or languages.
Definitions of Terms
- Neural Network: A computational model designed to recognize patterns by mimicking the human brain.
- Speech Recognition: The process of converting spoken language into text using computational methods.
- Deep Learning: A subset of machine learning involving neural networks with multiple layers.
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