Refining algorithmic estimation of relative fundamental frequency: Accounting for sample characteristics and fundamental frequency estimation method

Refining algorithmic estimation of relative fundamental frequency: Accounting for sample characteristics and fundamental frequency estimation method

Relative fundamental frequency (RFF) is a promising acoustic measure for evaluating voice disorders. Yet, the accuracy of the current RFF algorithm varies across a broad range of vocal signals. The authors investigated how fundamental frequency (fo) estimation and sample characteristics impact the r...

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Bibliographic Details
Main Authors: Buckley, D.P (Author), Kolin, K.R (Author), Noordzij, J.P (Author), Segina, R.K (Author), Stepp, C.E (Author), Tardif, M.C (Author), Vojtech, J.M (Author)
Format: Article
Language:English
Published: Acoustical Society of America 2019
Subjects:
acoustics
Acoustics
algorithm
Algorithm threshold
Algorithms
Autocorrelation methods
Automation
Category specifics
Estimation techniques
Frequency estimation
Fundamental frequencies
Fundamental frequency estimation
human
Humans
Mean bias errors
Mean square error
Natural frequencies
pathophysiology
phonation
Phonation
procedures
Refining
Root mean square errors
speech analysis
Speech Production Measurement
voice
voice disorder
Voice Disorders
Voice Quality
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