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: 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.