Hey Siri: How Effective are Common Voice Recognition Systems at Recognizing Dysphonic Voices?

Rohlfing, Matthew L; Buckley, Daniel P; Piraquive, Jacquelyn; Stepp, Cara E; Tracy, Lauren F

OBJECTIVES/HYPOTHESIS Interaction with voice recognition systems, such as Siriâ„¢ and Alexaâ„¢, is an increasingly important part of everyday life. Patients with voice disorders may have difficulty with this technology, leading to frustration and reduction in quality of life. This study evaluates the ability of common voice recognition systems to transcribe dysphonic voices. STUDY DESIGN Retrospective evaluation of "Rainbow Passage" voice samples from patients with and without voice disorders. METHODS Participants with (n = 30) and without (n = 23) voice disorders were recorded reading the "Rainbow Passage". Recordings were played at standardized intensity and distance-to-dictation programs on Apple iPhone 6Sâ„¢, Apple iPhone 11 Proâ„¢, and Google Voiceâ„¢. Word recognition scores were calculated as the proportion of correctly transcribed words. Word recognition scores were compared to auditory-perceptual and acoustic measures. RESULTS Mean word recognition scores for participants with and without voice disorders were, respectively, 68.6% and 91.9% for Apple iPhone 6Sâ„¢ (P < .001), 71.2% and 93.7% for Apple iPhone 11 Proâ„¢ (P < .001), and 68.7% and 93.8% for Google Voiceâ„¢ (P < .001). There were strong, approximately linear associations between CAPE-V ratings of overall severity of dysphonia and word recognition score, with correlation coefficients (R2 ) of 0.609 (iPhone 6Sâ„¢), 0.670 (iPhone 11 Proâ„¢), and 0.619 (Google Voiceâ„¢). These relationships persisted when controlling for diagnosis, age, gender, fundamental frequency, and speech rate (P < .001 for all systems). CONCLUSION Common voice recognition systems function well with nondysphonic voices but are poor at accurately transcribing dysphonic voices. There was a strong negative correlation with word recognition scores and perceptual voice evaluation. As our society increasingly interfaces with automated voice recognition technology, the needs of patients with voice disorders should be considered. LEVEL OF EVIDENCE IV Laryngoscope, 2020.