Quantifying dysphonia severity using a spectral/cepstral-based acoustic index: Comparisons with auditory-perceptual judgements from the CAPE-V

Abstract

This study investigated the relationship between acoustic spectral/cepstral measures and listener severity ratings in normal and disordered voice samples. CAPE-V sentence samples and the vowel /a/were elicited from eight normal speakers and 24 patients with varying degrees of dysphonia severity. Samples were analysed for measures of the cepstral peak prominence (CPP), the ratio of low-to-high spectral energy, and their respective standard deviations. Perceptual ratings of overall severity were also obtained for all samples. Results showed that all acoustic variables combined in a four-factor model which correlated with perceived severity with R = 0.81 (R(2) = 0.65). For the vowel /a/, a five-factor model incorporating all acoustic variables and gender correlated with perceived severity with R = 0.96 (R(2) = 0.91). Results indicate that a strong relationship between perceptual and acoustic estimates of dysphonia severity can be achieved in both continuous speech and vowel contexts using a model incorporating spectral/cepstral measures.

Acoustic Measures of Phonation during Connected Speech in Adductor Spasmodic Dysphonia

“…It should also be recognized that the hand editing process for retention of f 0 data was highly labor intensive and time consuming, and may therefore be of limited clinical applicability. The advent of cepstral analysis applied to connected speech [34,35] may prove to be a more efficient method for monitoring aperiodicity in ADSD in future studies, although these approaches necessarily incorporate all speech segments and don't focus exclusively on phonation in the manner that the present study did. Alternatively, the use of harmonic amplitude differences (e.g., H1-H2) also offers a viable strategy for quantifying voice in connected speech of ADSD [36].…”

Integration of Motor Learning Principles Into Real-Time Ambulatory Voice Biofeedback and Example Implementation Via a Clinical Case Study With Vocal Fold Nodules

“…Such novel biofeedback targets could be based on additional measures that can be extracted from the accelerometer, such as those generated from machine learning (Ghassemi et al, 2014), cepstral-and spectral-based measures (Awan et al, 2010), parameters using impedance-based inverse filtering (Llico et al, 2015;Zañartu, Ho, Mehta, Hillman, & Wodicka, 2013), and combinations of different subsets of these measures. Last, because the VHM records the raw acceleration signal, which is then stored in a database, it can be reprocessed and used in simulations to gain new insights as novel measures are developed or as new salient aspects of the acceleration waveform are determined.…”

“…The inclusion of a lower limit and upper limit threshold for every feature will permit biofeedback provision that is based on a desired range that avoids extremes; all current voice monitors can provide feedback only above a threshold or below a threshold, not both. The potential benefit of two limits can be illustrated in a hypothetical example using cepstral peak prominence (CPP), a popular clinically used measure (Awan, Roy, Jetté, Meltzner, & Hillman, 2010;Murphy, 2006;Murphy & Akande, 2007). Avoiding extremes of CPP may be clinically beneficial because low values of CPP are correlated to breathy or rough voicing and high values of CPP have been associated with pressed voicing (Awan et al, 2010;Shue, Chen, & Alwan, 2010).…”

“…The potential benefit of two limits can be illustrated in a hypothetical example using cepstral peak prominence (CPP), a popular clinically used measure (Awan, Roy, Jetté, Meltzner, & Hillman, 2010;Murphy, 2006;Murphy & Akande, 2007). Avoiding extremes of CPP may be clinically beneficial because low values of CPP are correlated to breathy or rough voicing and high values of CPP have been associated with pressed voicing (Awan et al, 2010;Shue, Chen, & Alwan, 2010). Duration hold allows for duration threshold times that are longer than average speech-related voiced durations; for example, the most frequently occurring voiced duration in speech is approximately 150 ms to 200 ms (Titze, Hunter, & Švec, 2007; Figure 2.…”

Nonlinear Acoustic Analysis in the Evaluation of Occupational Voice Disorders

“…2). Błąd funkcji dla charakterystyki Sammona wynosił ok. 1,26 (10)(11)(12)(13)(14). Po zastosowaniu MDC zaburzenia głosu u osób z łagodnymi zmianami fałdów głosowych zostały wykryte z dobrą czuło-ścią i specyficznością.…”