Review of Deep Speech Recognizer using Transcriber

This research presents the development of a “Deep Speech Recognizer using Transcriber,” aimed at streamlining the process of capturing and converting live speech into text. The primary problem addressed by this system is the need for efficient and accurate transcription in various contexts, such as meetings, lectures, and interviews. The methodology employed in this project utilizes Jupyter widgets for user interaction, PyAudio for microphone audio capture, Vosk for speech recognition, and recasepunc for punctuation enhancement. Key findings of this research include the successful implementation of a user-friendly system capable of recording and transcribing live speech in real-time. The system exhibits commendable accuracy in speech recognition, making it a valuable tool for professionals and researchers in fields requiring transcription services. Furthermore, the integration of recasepunc significantly improves the readability and usability of the transcribed text. This research contributes to the automation of speech transcription, offering a practical solution to the time-consuming and error-prone process of manual transcription. The system’s versatility and ease of use make it a promising tool for enhancing productivity and accessibility in various domains. Future work may involve further optimization and the exploration of additional features to expand the system’s capabilities and applications.