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It is estimated that around 70 million people worldwide are affected by aspeech disorder called stuttering. With recent advances in Automatic SpeechRecognition (ASR), voice assistants are increasingly useful in our everydaylives. Many technologies in education, retail, telecommunication and healthcarecan now be operated through voice. Unfortunately, these benefits are notaccessible for People Who Stutter (PWS). We propose a simple but effectivemethod called 'Detect and Pass' to make modern ASR systems accessible forPeople Who Stutter in a limited data setting. The algorithm uses a contextaware classifier trained on a limited amount of data, to detect acoustic framesthat contain stutter. To improve robustness on stuttered speech, this extrainformation is passed on to the ASR model to be utilized during inference. Ourexperiments show a reduction of 12.18% to 71.24% in Word Error Rate (WER)across various state of the art ASR systems. Upon varying the threshold of theassociated posterior probability of stutter for each stacked frame used indetermining low frame rate (LFR) acoustic features, we were able to determinean optimal setting that reduced the WER by 23.93% to 71.67% across differentASR systems.

Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass