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Formant-Aware Spectral Analysis of Sustained Vowels of Pathological Breathy Voice
Takeshi Ikuma1, Andrew J McWhorter1, Evrim Oral2
1Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, Louisiana; Voice Center, The Our Lady of The Lake Regional Medical Center, Baton Rouge, Louisiana.
Objectives:
This paper reports the effectiveness of formant-aware spectral parameters to predict the perceptual breathiness rating. A breathy voice has a steeper spectral slope and higher turbulent noise than a normal voice. Measuring spectral parameters of acoustic signals over lower formant regions is a known approach to capture the properties related to breathiness. This study examines this approach by testing the contemporary spectral parameters and algorithms within the framework, alternate frequency band designs, and vowel effects.
Methods:
Sustained vowel recordings (/a/, /i/, and /u/) of speakers with voice disorders in the German Saarbrueken Voice Database were considered (n: 367). Recordings with signal irregularities, such as subharmonics or with roughness perception, were excluded from the study. Four speech language pathologists perceptually rated the recordings for breathiness on a 100-point scale, and their averages were used in the analysis. The acoustic spectra were segmented into four frequency bands according to the vowel formant structures. Five spectral parameters (intraband harmonics-to-noise ratio, HNR; interband harmonics ratio, HHR; interband noise ratio, NNR; and interband glottal-to-noise energy, GNE, ratio) were evaluated in each band to predict the perceptual breathiness rating. Four HNR algorithms were tested.
Results:
Multiple linear regression models of spectral parameters, led by the HNRs, were shown to explain up to 85% of the variance in perceptual breathiness ratings. This performance exceeded that of the acoustic breathiness index (82%). Individually, the HNR over the first two formants best explained the variances in the breathiness (78%), exceeding the smoothed cepstrum peak prominence (74%). The performance of HNR was highly algorithm dependent (10% spread). Some vowel effects were observed in the perceptual rating (higher for /u/), predictability (5% lower for /u/), and model parameter selections.
Conclusions:
Strong per-vowel breathiness acoustic models were found by segmenting the spectrum to isolate the portion most affected by breathiness.
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