Speech and text recognition system: A tool for authentication based on random read
Abstract
The main objective of this paper is the development of a speech and text recognition system to improve security in user identification. For the development of the system, deep learning methodologies and several Python libraries were implemented, including Speech_recognition, Pyttsx3, and Librosa, among others. The system was evaluated in a controlled environment using 50 speech samples, obtaining an accuracy of 74%. The results indicated that 61.53% of the errors were due to failures in voice identification and 30.76% were due to discrepancies in matching the generated text. These findings underscore the overall effectiveness of the system, although they also point to the need to adjust the similarity thresholds and improve the recognition algorithms to increase their accuracy and robustness. It is concluded that the system presents a promising solution for biometric voice authentication, showing a balance between accuracy and areas for improvement that reinforce its usefulness in computer security applications.
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