Matthew L Rohlfing1, Daniel P Buckley1,2, Jacquelyn Piraquive1, Cara E Stepp2, Lauren F Tracy1. 1. Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center Boston University School of Medicine, Boston, Massachusetts, U.S.A. 2. Department of Speech, Language, and Hearing Sciences, Boston University, Boston, Massachusetts, U.S.A.
Abstract
OBJECTIVES/HYPOTHESIS: Interaction with voice recognition systems, such as Siri™ and Alexa™, 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 6S™, Apple iPhone 11 Pro™, and Google Voice™. 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 6S™ (P < .001), 71.2% and 93.7% for Apple iPhone 11 Pro™ (P < .001), and 68.7% and 93.8% for Google Voice™ (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 6S™), 0.670 (iPhone 11 Pro™), and 0.619 (Google Voice™). 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: 4 Laryngoscope, 131:1599-1607, 2021.
OBJECTIVES/HYPOTHESIS: Interaction with voice recognition systems, such as Siri™ and Alexa™, 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 6S™, Apple iPhone 11 Pro™, and Google Voice™. 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 6S™ (P < .001), 71.2% and 93.7% for Apple iPhone 11 Pro™ (P < .001), and 68.7% and 93.8% for Google Voice™ (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 6S™), 0.670 (iPhone 11 Pro™), and 0.619 (Google Voice™). 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: 4 Laryngoscope, 131:1599-1607, 2021.
Authors: Gail B Kempster; Bruce R Gerratt; Katherine Verdolini Abbott; Julie Barkmeier-Kraemer; Robert E Hillman Journal: Am J Speech Lang Pathol Date: 2008-10-16 Impact factor: 2.408
Authors: Matthew R Naunheim; Leanne Goldberg; Jennifer B Dai; Benjamin J Rubinstein; Mark S Courey Journal: Laryngoscope Date: 2019-06-20 Impact factor: 3.325