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Vojtech JM, Cilento DD, Luong AT, Noordzij JP, Diaz-Cadiz M, Groll MD, Buckley DP, McKenna VS, Noordzij JP, Stepp CE. Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets based on Vocal Fold Vibratory Measures. APPLIED SCIENCES (BASEL, SWITZERLAND) 2021; 11:3816. [PMID: 36188437 PMCID: PMC9524108 DOI: 10.3390/app11093816] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Methods for automating relative fundamental frequency (RFF)-an acoustic estimate of laryngeal tension-rely on manual identification of voiced/unvoiced boundaries from acoustic signals. This study determined the effect of incorporating features derived from vocal fold vibratory transitions for acoustic boundary detection. Simultaneous microphone and flexible nasendoscope recordings were collected from adults with typical voices (N=69) and with voices characterized by excessive laryngeal tension (N=53) producing voiced-unvoiced-voiced utterances. Acoustic features that coincided with vocal fold vibratory transitions were identified and incorporated into an automated RFF algorithm ("aRFF-APH"). Voiced/unvoiced boundary detection accuracy was compared between the aRFF-APH algorithm, a recently published version of the automated RFF algorithm ("aRFF-AP"), and gold-standard, manual RFF estimation. Chi-square tests were performed to characterize differences in boundary cycle identification accuracy among the three RFF estimation methods. Voiced/unvoiced boundary detection accuracy significantly differed by RFF estimation method for voicing offsets and onsets. Of 7721 productions, 76.0% of boundaries were accurately identified via the aRFF-APH algorithm, compared to 70.3% with the aRFF-AP algorithm and 20.4% with manual estimation. Incorporating acoustic features that corresponded with voiced/unvoiced boundaries led to improvements in boundary detection accuracy that surpassed the gold-standard method for calculating RFF.
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Affiliation(s)
- Jennifer M. Vojtech
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
- Delsys, Inc. and Altec, Inc., Natick, MA, 01760, USA
| | - Dante D. Cilento
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - Austin T. Luong
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - Jacob P. Noordzij
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - Manuel Diaz-Cadiz
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - Matti D. Groll
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - Daniel P. Buckley
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
- Department of Otolaryngology – Head and Neck Surgery, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Victoria S. McKenna
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
| | - J. Pieter Noordzij
- Department of Otolaryngology – Head and Neck Surgery, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Cara E. Stepp
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215, USA
- Department of Otolaryngology – Head and Neck Surgery, Boston University School of Medicine, Boston, Massachusetts, USA
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