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Objectives: 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.
Study Design: 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.
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http://dx.doi.org/10.1016/j.jvoice.2023.12.002 | DOI Listing |
J Voice
December 2023
Department of Otorhinolaryngology and Head & Neck Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.
Objectives: 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.
View Article and Find Full Text PDFSensors (Basel)
July 2023
IDLab, Ghent University-imec, 9000 Gent, Belgium.
Using the source-filter model of speech production, clean speech signals can be decomposed into an excitation component and an envelope component that is related to the phoneme being uttered. Therefore, restoring the envelope of degraded speech during speech enhancement can improve the intelligibility and quality of output. As the number of phonemes in spoken speech is limited, they can be adequately represented by a correspondingly limited number of envelopes.
View Article and Find Full Text PDFJ Voice
September 2024
Department of Signal Processing and Acoustics, Aalto University, Finland.
Automatic voice pathology detection is a research topic, which has gained increasing interest recently. Although methods based on deep learning are becoming popular, the classical pipeline systems based on a two-stage architecture consisting of a feature extraction stage and a classifier stage are still widely used. In these classical detection systems, frame-wise computation of mel-frequency cepstral coefficients (MFCCs) is the most popular feature extraction method.
View Article and Find Full Text PDFSensors (Basel)
July 2021
Autonomous and Intelligent Systems Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model-support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker's gender and his/her identity.
View Article and Find Full Text PDFJ Voice
January 2020
Department of Statistics, Penn State University, University Park, Pennsylvania. Electronic address:
Objectives: 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/F cutoffs would result in a CPP difference that would be characteristic of the rough, diplophonic voice type.
Methodology: Central one-second portions of sustained vowel /a/ productions were obtained from 90 subjects (rough, breathy, and normophonic voices).
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