Speech recognition has gained much attention from researchers for almost last two decades. Isolated words, connected words, and continuous speech are the main focused areas of speech recognition. Researchers have adopted many techniques to solve speech recognition challenges under the umbrella of Artificial Intelligence (AI), Pattern Recognition and Acoustic Phonetic approaches. Variation in pronunciation of words, individual accents, unwanted ambient noise, speech context, and quality of input devices are some of these challenges in speech recognition. Many Application Programming Interface (API)s are developed to overcome the issue of accuracy in a speech-to-text conversion such as Microsoft Speech API and Google Speech API. In this paper, the performance of Microsoft Speech API is analyzed against other Speech APIs mentioned in the literature on the special dataset (without background noise) prepared. A Voice Interactive Speech to Text (VIST) audio player was developed for the analysis of Microsoft Speech API. VIST audio player creates runtime subtitles of the audio files running on it; the player is responsible for speech to text conversion in real-time. Microsoft Speech API was incorporated in the application to validate and make the performance of API measurable. The experiments proved the Microsoft Speech API more accurate with respect to other APIs in the context of the prepared dataset for the VIST audio player. The accuracy rate according to the precision-recall is 96% for Microsoft Speech API, which is better than previous ones as mentioned in the literature.