Speech Mastery Detection Using Advanced Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) Techniques

In today's globalized world, English communication skills are essential for career advancement and cross-cultural collaboration, enhancing access to information and opportunities. Automatic speech recognition, or ASR, is a separate machine-driven method for transcription and decoding spoken language. An ASR system typically uses a microphone to capture a speaker's audio input, analyze it using a model, algorithm, or pattern, and output the results, which are often text messages (Lai, Karat, Yankelovich, 2008). This paper provides a thorough method for utilizing Python-based tools and modules to extract and analyze linguistic characteristics from audio data. The suggested approach turns spoken language into text using voice recognition technology, and then it uses natural language processing (NLP) methods to extract different linguisticmetrics. Word count, sentence count, vocabulary size, average sentence length, average word length, sentiment score, speech pace, frequency of pauses, and average length of pauses are some of these measures. The technique also determines the speaker's speaking style. Keywords: audio analysis, linguistic features, natural language processing, pause detection, speech recognition, sentiment analysis, visualization.