A Comparison of Online Automatic Speech Recognition Systems and the Nonverbal Responses to Unintelligible Speech (1904.12403v1)

Abstract: Automatic Speech Recognition (ASR) systems have proliferated over the recent years to the point that free platforms such as YouTube now provide speech recognition services. Given the wide selection of ASR systems, we contribute to the field of automatic speech recognition by comparing the relative performance of two sets of manual transcriptions and five sets of automatic transcriptions (Google Cloud, IBM Watson, Microsoft Azure, Trint, and YouTube) to help researchers to select accurate transcription services. In addition, we identify nonverbal behaviors that are associated with unintelligible speech, as indicated by high word error rates. We show that manual transcriptions remain superior to current automatic transcriptions. Amongst the automatic transcription services, YouTube offers the most accurate transcription service. For non-verbal behavioral involvement, we provide evidence that the variability of smile intensities from the listener is high (low) when the speaker is clear (unintelligible). These findings are derived from videoconferencing interactions between student doctors and simulated patients; therefore, we contribute towards both the ASR literature and the healthcare communication skills teaching community.