OBJECTIVE: Hoarseness is primarily perceived as breathiness or roughness. Despite the various tools that quantitatively assess hoarseness, roughness has been difficult to quantify because of its complex acoustic structure, such as subharmonics. The parameter obtained from the two-stage cepstral analysis is promising for evaluating roughness. Thus, this study aimed to improve the accuracy of the parameter using a customized pitch setting and investigate the relationship between roughness and subharmonics.
METHODS: The design is a retrospective study.
METHODS: Two-stage cepstral analysis was used to analyze the voice recordings of 455 participants, speech impaired and normal controls, using the Analysis of Dysphonia in Speech and Voice and Praat software. For validation, the ground truth of subharmonics was visually quantified using a narrowband spectrogram. The reliability and validity of the two-stage cepstral analysis and subharmonics measures on spectrograms were evaluated.
RESULTS: The two-stage cepstral analysis showed a very strong correlation (r = 0.963) between the two software programs. Intra- and inter-rater reliability of the subharmonics measures on spectrograms were also good. Two-stage cepstral analysis showed that even with customized pitch settings, the diagnostic systems and correlations for perceptual roughness and subharmonics were weak to moderate. The subharmonics measures on spectrograms showed a strong correlation with roughness and moderate diagnostic accuracy of subharmonics.
CONCLUSIONS: The two-stage cepstral analysis showed some improvement in diagnostic accuracy and correlation with customized pitch settings, but it did not sufficiently detect subharmonics or roughness. The analysis using subharmonics measures on spectrograms proved the high correlation between subharmonics and roughness, indicating that developing acoustic analysis parameters that sufficiently detect subharmonics is necessary.

OBJECTIVE: The objective of this study was to investigate the ability of a two-stage method of cepstral peak identification to effectively discriminate rough vs breathy vs typical voice in sustained vowel productions. It was hypothesized that a dual-stage search for cepstral peak prominences (CPP\'s) above and below specified quefrency/F0 cutoffs would result in a CPP difference that would be characteristic of the rough, diplophonic voice type.
METHODS: Central one-second portions of sustained vowel /a/ productions were obtained from 90 subjects (rough, breathy, and normophonic voices). All voice samples were analyzed using a a two-stage cepstral analysis process in which a CPPHigh-Low difference value was obtained by identifying cepstral peaks above and below a lower limit for expected F0 (150 Hz for females and 90 Hz for males), called CPPHigh and CPPLow respectively.
RESULTS: The CPPHigh-Low difference value was observed to be a highly significant predictor, with negative values for this parameter characteristic of a dominant subharmonic in the voice signal and the perception of diplophonic, rough voice. Correct classification of rough vs nonrough voice samples was 82.2% (sensitivity 0.80 and specificity 0.833). In the consideration of three group classification (breathy vs. normophonic vs. rough), models incorporating two predictors (the CPP obtained from a single search through a 60 to 300 Hz frequency range (CPPDefault) and the CPPHigh-Low difference value) correctly classified 78.88% of the voice samples.
CONCLUSIONS: Rough, diplophonic voices were consistently observed to have a subharmonic peak that was greater in amplitude than the cepstral peak obtained within the region of the expected F0, resulting in a negative value for the CPPHigh-Low difference. The two-stage cepstral analysis process described herein is visually intuitive from the graphical display of a cepstrum and is a simple extended calculation derived from cepstral analysis procedures that have been recommended as essential in the acoustic description of vocal quality.

Diplophonia can occur in patients with polyps, atrophy, paralysis, or scars. Its vibratory patterns have not been well characterized. High-speed video (HSV) analysis can contribute to their understanding. Twenty subjects with a diplophonic voice quality were studied by HSV. Diplophonia was due to medical causes including vocal fold paresis (n = 7), vocal atrophy (n = 5), polyps (n = 5), and scars/sulci (n = 3). The HSV was analyzed using a multislice digital videokymography (DKG). The DKG tracing was analyzed qualitatively and then transformed into a vibrogram waveform signal for frequency analysis. RESULTS: Vibratory abnormalities seen on HSVs explained the diplophonia. Subharmonics to the fundamental frequency can be visualized by DKG. None could be resolved by stroboscopy. One can stratify diplophonia as symmetric or asymmetric based on the involvement of one or both vocal folds. Scars and atrophy showed symmetric subharmonic production with ectopic beats every 4-10 beats. Some subjects showed anterior and posterior independent vocal fold oscillators. Asymmetric causes of diplophonia are common in patients with paralysis. Two different oscillation frequencies of each vocal fold generate in and then out of phase interaction between the two sides. Vibrogram analysis documents the frequent presence of interharmonic energy peaks above the dominant fundamental frequency. Eighteen of the 20 subjects have obvious subharmonic peaks. CONCLUSION: Patients with diplophonia have vibratory abnormalities arising from the vocal folds. HSV and vibrogram analysis followed by frequency analysis of the vibrogram can resolve vibratory abnormality into symmetric versus asymmetric causes and can document the type of vibratory abnormality.

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.

OBJECTIVE: Automatic acoustic measures of voice quality in people with Down syndrome (DS) do not reliably reflect perceived voice qualities. This study used acoustic data and visual spectral data to investigate the relationship between perceived voice qualities and acoustic measures.
METHODS: Participants were four young adults (two males, two females; mean age 23.8 years) with DS and severe learning disabilities, at least one of whom had a hearing impairment.
METHODS: Participants imitated sustained /i/, /u/, and /a/ vowels at predetermined target pitches within their vocal range. Medial portions of vowels were analyzed, using Praat, for fundamental frequency, harmonics-to-noise ratio, jitter, and shimmer. Spectrograms were used to identify the presence and the duration of subharmonics at onset and offset, and mid-vowel. The presence of diplophonia was assessed by auditory evaluation.
RESULTS: Perturbation data were highest for /a/ vowels and lowest for /u/ vowels. Intermittent productions of subharmonics were evident in spectrograms, some of which coincided with perceived diplophonia. The incidence, location, duration, and intensity of subharmonics differed between the four participants.
CONCLUSIONS: Although the acoustic data do not clearly indicate atypical phonation, diplophonia and subharmonics reflect nonmodal phonation. The findings suggest that these may contribute to different perceived voice qualities in the study group and that these qualities may result from intermittent involvement of supraglottal structures. Further research is required to confirm the findings in the wider DS population, and to assess the relationships between voice quality, vowel type, and physiological measures.

OBJECTIVE: Diplophonia is an often misinterpreted symptom of disordered voice, and needs objectification. An audio signal processing algorithm for the detection of diplophonia is proposed. Diplophonia is produced by two distinct oscillators, which yield a profound physiological interpretation. The algorithm\'s performance is compared with the clinical standard parameter degree of subharmonics (DSH).
METHODS: This is a prospective study.
METHODS: A total of 50 dysphonic subjects with (28 with diplophonia and 22 without diplophonia) and 30 subjects with euphonia were included in the study. From each subject, up to five sustained phonations were recorded during rigid telescopic high-speed video laryngoscopy. A total of 185 phonations were split up into 285 analysis segments of homogeneous voice qualities. In accordance to the clinical group allocation, the considered segmental voice qualities were (1) diplophonic, (2) dysphonic without diplophonia, and (3) euphonic. The Diplophonia Diagram is a scatter plot that relates the one-oscillator synthesis quality (SQ1) to the two-oscillator synthesis quality (SQ2). Multinomial logistic regression is used to distinguish between diplophonic and nondiplophonic segments.
RESULTS: Diplophonic segments can be well distinguished from nondiplophonic segments in the Diplophonia Diagram because two-oscillator synthesis is more appropriate for imitating diplophonic signals than one-oscillator synthesis. The detection of diplophonia using the Diplophonia Diagram clearly outperforms the DSH by means of positive likelihood ratios (56.8 versus 3.6).
CONCLUSIONS: The diagnostic accuracy of the newly proposed method for detecting diplophonia is superior to the DSH approach, which should be taken into account for future clinical and scientific work.