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Automatic Speech Recognition (ASR) systems have proliferated over the recentyears to the point that free platforms such as YouTube now provide speechrecognition services. Given the wide selection of ASR systems, we contribute tothe field of automatic speech recognition by comparing the relative performanceof two sets of manual transcriptions and five sets of automatic transcriptions(Google Cloud, IBM Watson, Microsoft Azure, Trint, and YouTube) to helpresearchers to select accurate transcription services. In addition, we identifynonverbal behaviors that are associated with unintelligible speech, asindicated by high word error rates. We show that manual transcriptions remainsuperior to current automatic transcriptions. Amongst the automatictranscription services, YouTube offers the most accurate transcription service.For non-verbal behavioral involvement, we provide evidence that the variabilityof smile intensities from the listener is high (low) when the speaker is clear(unintelligible). These findings are derived from videoconferencinginteractions between student doctors and simulated patients; therefore, wecontribute towards both the ASR literature and the healthcare communicationskills teaching community.

A Comparison of Online Automatic Speech Recognition Systems and the Nonverbal Responses to Unintelligible Speech