Pasquale Bottalico1, Juliana Codino2, Lady Catherine Cantor-Cutiva3, Katherine Marks2, Charles J Nudelman4, Jean Skeffington2, Rahul Shrivastav5, Maria Cristina Jackson-Menaldi6, Eric J Hunter7, Adam D Rubin2. 1. Department of Speech and Hearing Science, University of Illinois Urbana-Champaign, Champaign, Illinois. Electronic address: pb81@illinois.edu. 2. Lakeshore Ear, Nose, and Throat Center, Lakeshore Professional Voice Center, Michigan. 3. Department of Collective Health, Universidad Nacional de Colombia, Bogota, Colombia; Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, Michigan. 4. Department of Speech and Hearing Science, University of Illinois Urbana-Champaign, Champaign, Illinois. 5. University of Georgia, Atlanta, Georgia. 6. Lakeshore Ear, Nose, and Throat Center, Lakeshore Professional Voice Center, Michigan; Department of Otolaryngology, School of Medicine, Wayne State University, Detroit, Michigan. 7. Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, Michigan.
Abstract
INTRODUCTION: Computer analysis of voice recordings is an integral part of the evaluation and management of voice disorders. In many practices, voice samples are taken in rooms that are not sound attenuated and/or sound-proofed; further, the technology used is rarely consistent. This will likely affect the recordings, and therefore, their analyses. OBJECTIVES: The objective of this study is to compare various acoustic outcome measures taken from samples recorded in a sound-proofed booth to those recorded in more common clinic environments. Further, the effects from six different commonly used microphones will be compared. METHODS: Thirty-six speakers were recorded while reading a text and producing sustained vowels in a controlled acoustic environment. The collected samples were reproduced by a Head and Torso Simulator and recorded in three clinical rooms and in a sound booth using six different microphones. Newer measures (eg, Pitch Strength, cepstral peak prominence, Acoustic Voice Quality Index), as well as more traditional measures (eg Jitter, Shimmer, harmonics-to-noise ratio and Spectrum Tilt), were calculated from the samples collected with each microphone and within each room. RESULTS: The measures which are more robust to room acoustic differences, background noise, and microphone quality include Jitter and smooth cepstral peak prominence, followed by Shimmer, Acoustic Voice Quality Index, harmonics-to-noise ratio, Pitch Strength, and Spectrum Tilt. CONCLUSIONS: The effect of room acoustics and background noise on voice parameters appears to be stronger than the type of microphone used for the recording. Consequently, an appropriate acoustical clinical space may be more important than the quality of the microphone.
INTRODUCTION: Computer analysis of voice recordings is an integral part of the evaluation and management of voice disorders. In many practices, voice samples are taken in rooms that are not sound attenuated and/or sound-proofed; further, the technology used is rarely consistent. This will likely affect the recordings, and therefore, their analyses. OBJECTIVES: The objective of this study is to compare various acoustic outcome measures taken from samples recorded in a sound-proofed booth to those recorded in more common clinic environments. Further, the effects from six different commonly used microphones will be compared. METHODS: Thirty-six speakers were recorded while reading a text and producing sustained vowels in a controlled acoustic environment. The collected samples were reproduced by a Head and Torso Simulator and recorded in three clinical rooms and in a sound booth using six different microphones. Newer measures (eg, Pitch Strength, cepstral peak prominence, Acoustic Voice Quality Index), as well as more traditional measures (eg Jitter, Shimmer, harmonics-to-noise ratio and Spectrum Tilt), were calculated from the samples collected with each microphone and within each room. RESULTS: The measures which are more robust to room acoustic differences, background noise, and microphone quality include Jitter and smooth cepstral peak prominence, followed by Shimmer, Acoustic Voice Quality Index, harmonics-to-noise ratio, Pitch Strength, and Spectrum Tilt. CONCLUSIONS: The effect of room acoustics and background noise on voice parameters appears to be stronger than the type of microphone used for the recording. Consequently, an appropriate acoustical clinical space may be more important than the quality of the microphone.
Authors: Philip C Hannaford; Julie A Simpson; Ann Fiona Bisset; Adrian Davis; William McKerrow; Robert Mills Journal: Fam Pract Date: 2005-03-16 Impact factor: 2.267
Authors: Lisa M Kopf; Cristina Jackson-Menaldi; Adam D Rubin; Jean Skeffington; Eric J Hunter; Mark D Skowronski; Rahul Shrivastav Journal: J Voice Date: 2017-03-17 Impact factor: 2.009