Hybrid Transformer and Rule-Based NLP for Robust Language and Speech Applications

Aim: The aim of this study is to improve voice analysis and grammatical correction; this study suggests an Artificial Intelligence (AI) framework that combines the Text-To-Text Transfer Transformer (T5) neural network with the SpaCy Natural Language Processing (NLP) library. Materials and Methods: The experimental setup contrasts Group 1 (Control), which uses the existing SpaCy v3.6’s grammar checker in conjunction with Term Frequency–Inverse Document Frequency (TF-IDF) classifiers with limited accuracy of 27 samples. Group 2(intervention) The integrated SpaCy with T5-base (optimized on Corpus of Linguistic Acceptability (CoLA)/Sentiment140) and tested on the same text samples along with 100 LibriSpeech clips to increase the accuracy of grammar correction. Results: The integrated system achieved a 95% increase in grammatical correction accuracy, a 7.5% decrease in speech-to-text Word Error Rate (WER), and a 92% increase in sentiment analysis precision. Conclusion: The combination of SpaCy and T5 significant improvements in language understanding and speech analysis, setting a standard for future multimodal NLP applications. The proposed method suggestively improves the grammar accuracy, decreases the speech-to-text errors, and increases the sentiment precision. So, it is appropriate for recent NLP applications.