Advanced Fluency Augmentation Framework for Stuttered Speech Recognition and Articulation Correction

Stuttering which is also known as stammering, is a speech disorder in which people suffer while communicating with disorders like prolonged words, syllables or phrases, repetitions and also sometimes stop while speaking or make no sound for a certain syllables. Current speech recognition systems such as Google Assistant, Apple’s Siri etc have very efficient Speech recognition system for normal speech, but they fail to recognise when the person stutters. This paper proposes to create a better algorithm for improving speech recognition in stuttering people. The suggested method uses an amplitude threshold obtained through neural network analysis to preprocess speech input in order to identify and eliminate disfluencies [2]. The major problem until now is that a system stops recognizing after a pause is encountered in the speech and hence, the average accuracy of stuttered speech recognition is around 70%. With a new algorithm which will take into account the words or characters after the pause and then use that also for recognition, the accuracy can be improved. This system is implemented in five stages namely, Amplitude thresholding and filtering, Silence ejection, speech to text conversion, repetition removal and text to speech (TTS) conversion.