DisfluencyFixer: A tool to enhance Language Learning through Speech To
  Speech Disfluency Correction
Vineet Bhat Preethi Jyothi Pushpak Bhattacharyya
Conversational speech often consists of deviations from the speech plan,
producing disfluent utterances that affect downstream NLP tasks. Removing these
disfluencies is necessary to create fluent and coherent speech. This paper
presents DisfluencyFixer, a tool that performs speech-to-speech disfluency
correction in English and Hindi using a pipeline of Automatic Speech
Recognition (ASR), Disfluency Correction (DC) and Text-To-Speech (TTS) models.
Our proposed system removes disfluencies from input speech and returns fluent
speech as output along with its transcript, disfluency type and total
disfluency count in source utterance, providing a one-stop destination for
language learners to improve the fluency of their speech. We evaluate the
performance of our tool subjectively and receive scores of 4.26, 4.29 and 4.42
out of 5 in ASR performance, DC performance and ease-of-use of the system. Our
tool can be accessed openly at the following link.