Abstract：
OBJECTIVE: Diplophonia is a common symptom of voice disorder that is in need of objectification. We investigated whether diplophonia can be detected from audio recordings of text readings by means of dedicated audio signal processing, ie, a descendant of a formerly published \"Diplophonia Diagram.\"
METHODS: Diagnostic study.
METHODS: Forty subjects were included who had been clinically rated in the past as diplophonic. For each subject, the audio signal of the German standard text \"Der Nordwind und die Sonne\" was recorded. First, subject groups regarding the frequency of occurrence of diplophonic episodes were established via manual labeling of audio recordings. Reference boundaries of diplophonic time intervals and the boundaries of voiced time intervals were manually obtained. Each time interval was labeled as diplophonic or nondiplophonic, as well as voiced or unvoiced. The diplophonia rate was defined as the total duration of diplophonation among the total duration of voiced phonation. Based on the diplophonia rate obtained from manual annotations, subjects were distinguished who were (1) frequently diplophonic, (2) unfrequently diplophonic, and (3) nondiplophonic during the reading of the standard text. Second, the grouping was predicted automatically via audio signal processing, and the performance of automatic prediction was evaluated. The audio recordings were analyzed with a purpose-built audio signal processor that estimated the diplophonia rate automatically. Two cut-off threshold classifiers were trained to detect automatically (1) frequently diplophonic, and (2) nondiplophonic subjects. In addition, multinomial logistic regression was performed to enable automatic 3-way classification.
RESULTS: Among all subjects, 14 were frequently diplophonic during the reading of the text, 14 were unfrequently diplophonic, and the remaining 12 were nondiplophonic. In automated detection of frequently diplophonic subjects, a sensitivity of 71% and a specificity of 88% were obtained. The sensitivity and specificity regarding automated detection of nondiplophonic subjects were 68% and 92%. In 3-way classification, 62.5% of the subjects were classified into the correct group.
CONCLUSIONS: Only two-thirds of the subjects who had been labeled as diplophonic on the base of auditory impression during clinical anamnesis diplophonated during the reading of a standard text. This demonstrates that the ecological validity of audio recordings of standard text readings is limited. Subject groups regarding the frequency of occurrence of diplophonic episodes were established and audio signal processing enabled automated classification. The observed performance of automated classification was promising and may be relevant to future clinical and scientific work. Possible applications include objective clinical voice assessment for diagnostic purposes and feedback based training of clinical raters.
METHODS: Diagnostic study.
METHODS: Forty subjects were included who had been clinically rated in the past as diplophonic. For each subject, the audio signal of the German standard text \"Der Nordwind und die Sonne\" was recorded. First, subject groups regarding the frequency of occurrence of diplophonic episodes were established via manual labeling of audio recordings. Reference boundaries of diplophonic time intervals and the boundaries of voiced time intervals were manually obtained. Each time interval was labeled as diplophonic or nondiplophonic, as well as voiced or unvoiced. The diplophonia rate was defined as the total duration of diplophonation among the total duration of voiced phonation. Based on the diplophonia rate obtained from manual annotations, subjects were distinguished who were (1) frequently diplophonic, (2) unfrequently diplophonic, and (3) nondiplophonic during the reading of the standard text. Second, the grouping was predicted automatically via audio signal processing, and the performance of automatic prediction was evaluated. The audio recordings were analyzed with a purpose-built audio signal processor that estimated the diplophonia rate automatically. Two cut-off threshold classifiers were trained to detect automatically (1) frequently diplophonic, and (2) nondiplophonic subjects. In addition, multinomial logistic regression was performed to enable automatic 3-way classification.
RESULTS: Among all subjects, 14 were frequently diplophonic during the reading of the text, 14 were unfrequently diplophonic, and the remaining 12 were nondiplophonic. In automated detection of frequently diplophonic subjects, a sensitivity of 71% and a specificity of 88% were obtained. The sensitivity and specificity regarding automated detection of nondiplophonic subjects were 68% and 92%. In 3-way classification, 62.5% of the subjects were classified into the correct group.
CONCLUSIONS: Only two-thirds of the subjects who had been labeled as diplophonic on the base of auditory impression during clinical anamnesis diplophonated during the reading of a standard text. This demonstrates that the ecological validity of audio recordings of standard text readings is limited. Subject groups regarding the frequency of occurrence of diplophonic episodes were established and audio signal processing enabled automated classification. The observed performance of automated classification was promising and may be relevant to future clinical and scientific work. Possible applications include objective clinical voice assessment for diagnostic purposes and feedback based training of clinical raters.
摘要：
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