Speech recognition software is designed to capture human-to-human conversations, either in real-time or asynchronously, and requires testing and evaluation to ensure accuracy. Automatic speech recognition (ASR) has come a long way in recent years, but evaluating its efficacy is crucial to avoid frustrating user experiences. To evaluate ASR, various metrics can be used, including word error rate, Levenshtein distance, number of word-level insertions, deletions, and mismatches, phrase level insertions, and general statistics about the original and generated files. These metrics provide a comprehensive understanding of an ASR system's accuracy and help identify areas for improvement. To create a more effective ASR system, using a speech recognition API with features such as real-time speech recognition, word-level timestamps, punctuation detection, speaker diarization, custom vocabulary, and sentence-level sentiment analysis can be beneficial. Additionally, customizable features like key phrase detection, pre-formatted transcripts, and named entity extraction can enhance the accuracy of ASR systems.