A Comparative Analysis of Real Time Open-Source Speech Recognition Tools for Social Robots

Pande, A.; Shrestha, Bhanu; Rani, Anshul; Mishra, Deepti

doi:10.1007/978-3-031-35708-4_26

Cited by 4 publications

(1 citation statement)

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“…Currently, many open-source speech-to-text conversion tools are available. Pande et al [19] compared various speech recognition tools, such as Google speech recognition, Vosk, CMUSphinx, DeepSpeech, and Whisper speech recognition tools on the basis of evaluation metrics and found that Whisper has the best accuracy among them. OpenAI developed Whisper [20], is an open-source speech recognition tool.…”

Section: Introductionmentioning

confidence: 99%

The Synergy between a Humanoid Robot and Whisper: Bridging a Gap in Education

Pande,

Mishra

2023

Electronics

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Students may encounter problems concentrating during a lecture due to various reasons, which can be related to the educator’s accent or the student’s auditory difficulties. This may lead to reduced participation and poor performance in the class. In this paper, we explored whether the incorporation of the humanoid robot Pepper can help in improving the learning experience. Pepper can capture the audio of a person; however, there is no guarantee of accuracy of the recorded audio due to various factors. Therefore, we investigated the limitations of Pepper’s speech recognition system with the aim of observing the effect of distance, age, gender, and the complexity of statements. We conducted an experiment with eight persons including five females and three males who spoke provided statements at different distances. These statements were classified using different statistical scores. Pepper does not have the functionality to transcribe speeches into text. To overcome this problem, we integrated Pepper with a speech-to-text recognition tool, Whisper, which transcribes speech into text that can be displayed on Pepper’s screen using its service. The purpose of the study is to develop a system where the humanoid robot Pepper and the speech-to-text recognition tool Whisper act in synergy to bridge the gap between verbal and visual communication in education. This system could be beneficial for students as they will better understand the content through the visual representation of the teacher’s spoken words regardless of any hearing impairments and accent problems. The methodology involves recording the participant’s speech, followed by its transcription to text by Whisper, and then evaluation of the generated text using various statistical scores. We anticipate that the proposed system will be able to increase the student’s learning experience, engagement, and immersion in a classroom environment.

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Section: Introductionmentioning

confidence: 99%