Optimization of Quantum-Inspired Neural Networks for Speech Recognition in Federal University, Dutsin-Ma, Katsina State
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- NGN 5000
Background of the Study
Speech recognition technology has made significant strides in recent years, enabling applications such as virtual assistants, automated transcription, and language translation. However, traditional neural networks used in speech recognition systems still face challenges in terms of accuracy, especially in noisy environments or when dealing with complex linguistic variations. Quantum computing has the potential to enhance neural network models through quantum-inspired algorithms, which could provide more efficient ways to process and classify speech data (Mandel et al., 2023). Quantum-inspired neural networks leverage quantum principles to simulate quantum-like behavior in classical computing systems, improving learning efficiency and pattern recognition.
At Federal University, Dutsin-Ma, Katsina State, where technological advancements in artificial intelligence are increasingly being emphasized, optimizing quantum-inspired neural networks for speech recognition could revolutionize the way speech data is processed. This study aims to explore how quantum-inspired algorithms can be integrated into neural network models to improve the accuracy and efficiency of speech recognition systems used in educational settings.
Statement of the Problem
Despite the advancements in speech recognition technology, many existing systems still struggle with accuracy and reliability, especially in challenging environments. Additionally, the computational cost of training and running large-scale speech recognition models can be prohibitively high. Quantum-inspired neural networks offer a potential solution by providing faster training times and improved generalization to new data. However, their application in speech recognition has not been thoroughly explored in Nigerian institutions. This study will examine how these advanced models can be optimized for speech recognition at Federal University, Dutsin-Ma.
Objectives of the Study
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To investigate the potential of quantum-inspired neural networks in improving speech recognition accuracy.
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To optimize quantum-inspired neural network models for speech recognition tasks at Federal University, Dutsin-Ma.
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To evaluate the efficiency and computational benefits of using quantum-inspired neural networks in speech recognition applications.
Research Questions
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How can quantum-inspired neural networks improve speech recognition accuracy in Nigerian educational settings?
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What optimization techniques can be applied to quantum-inspired neural networks for speech recognition at Federal University, Dutsin-Ma?
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What are the computational advantages of using quantum-inspired neural networks for speech recognition?
Significance of the Study
This research will contribute to the advancement of speech recognition technology by exploring the potential of quantum-inspired algorithms to enhance its performance. It will also provide Federal University, Dutsin-Ma, with insights on how to implement cutting-edge technology in speech recognition, benefiting both educational and research applications.
Scope and Limitations of the Study
This study will focus on optimizing quantum-inspired neural networks for speech recognition specifically within the context of Federal University, Dutsin-Ma, Katsina State. It will not extend to broader applications outside of the university.
Definitions of Terms
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Quantum-Inspired Neural Networks: Artificial neural networks that leverage principles from quantum computing to enhance performance in tasks like classification and pattern recognition.
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Speech Recognition: The process of converting spoken language into text using computational algorithms.
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Optimization: The process of improving a system’s performance, such as increasing the accuracy or reducing the computational cost of a neural network model.
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