An Optimal Method for Speech Recognition Based on Neural Network

Abstract

Natural language processing technologies have become more widely available in recent years, making them more useful in everyday situations. Machine learning systems that employ accessible datasets and corporate work to serve the whole spectrum of problems addressed in computational linguistics have lately yielded a number of promising breakthroughs. These methods were particularly advantageous for regional languages, as they were provided with cutting-edge language processing tools as soon as the requisite corporate information was generated. The bulk of modern people are unconcerned about the importance of reading. Reading aloud, on the other hand, is an effective technique for nourishing feelings as well as a necessary skill in the learning process. This paper proposed a novel approach for speech recognition based on neural networks. The attention mechanism is first utilized to determine the speech accuracy and fluency assessments, with the spectrum map as the feature extraction input. To increase phoneme identification accuracy, reading precision, for example, employs a new type of deep speech. It makes use of the exportchapter tool, which provides a corpus, as well as the TensorFlow framework in the experimental setting. The experimental findings reveal that the suggested model can more effectively assess spoken speech accuracy and reading fluency than the old model, and its evaluation model's score outcomes are more accurate.