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정상 성인에서 스마트폰 녹음을 위한 마이크 유형 간 음향학적 측정치 비교

A comparison of acoustic measures among the microphone types for smartphone recordings in normal adults

  • 박정인 (한림대학교 일반대학원 언어병리청각학과) ;
  • 이승진 (한림대학교 자연과학대학 언어청각학부 및 청각언어연구소)
  • Jeong In Park (Department of Speech Pathology & Audiology, Graduate School of Hallym University) ;
  • Seung Jin Lee (Division of Speech Pathology and Audiology, Research Institute of Audiology and Speech Pathology, College of Natural Sciences, Hallym University)
  • 투고 : 2024.04.29
  • 심사 : 2024.05.27
  • 발행 : 2024.06.30

초록

본 연구에서는 정상음성사용자를 대상으로 음성검사를 위한 고가의 음성 녹음 장비인 Computerized Speech Lab(CSL) 대신 스마트폰에 적용 가능한 단일지향성 유선 핀마이크(WIRED), 스마트폰의 자체 내장 무지향성 마이크(SMART), 블루투스 무선 이어폰인 갤럭시 버즈2 프로(WIRELESS)로 녹음된 음성샘플의 음향학적 측정치를 비교하고자 하였다. 연구대상은 최근 3개월 이내 호흡기 질환으로 이비인후과에 내원한 적이 없는 정상성인 40명(남 12명, 여 28명)이었으며, 소음이 통제된 방음 부스에서 모음 /아/ 연장 발성(4초) 과제와 '산책' 문장, '가을' 문단 읽기 과제를 네 가지의 기기로 동시에 녹음하였다. 4종의 샘플들에 대하여 CSL 녹음을 기준으로 동기화 작업을 진행하였으며, MDVP와 ADSV, VOXplot 프로그램을 이용하여 분석하였다. 연구 결과, F0, shimmer, noise-to-harmonic ratio를 제외한 다른 변수들에서 유의미한 차이가 있었다. 특히 SRV, SRS, CSIDV, CSIDS, AVQI의 경우 CSL에 비해 WIRED의 CSIDV, CSIDS, AVQI 중증도가 낮았던 반면, SMART에서는 높게 나타났다. SRV, SRS의 경우 반대의 경향이 나타났으며, WIRELESS는 과제에 따라 다른 경향이 있었다. CSL과 다른 마이크 유형들은 동일한 변수 간에는 모두 양의 상관관계를 보였으며, F0와 CPPV가 모든 유형에서 공히 강한 양의 상관관계를 보였다. ICC 또한 F0와 CPPV가 모두 0.9 이상으로 가장 높았다. 본 연구에서 사용된 마이크를 음향학적 분석을 위한 녹음 도구로 사용할 때, F0와 CPPV의 경우 신뢰도 높은 분석 변수로 마이크 유형과 무관하게 포함할 수 있고, SR, CSID, AVQI의 경우 마이크 유형에 따라 분석 및 해석에 주의를 기울일 필요가 있을 것으로 판단된다.

This study aimed to compare the acoustic measurements of speech samples recorded from individuals with normal voices using various devices: the Computerized Speech Lab (CSL), a unidirectional wired pin-microphone (WIRED) suitable for smartphones, the built-in omnidirectional microphone (SMART) of smartphones, and Bluetooth-connected wireless earphones, specifically the Galaxy Buds2 Pro (WIRELESS). This study included 40 normal adults (12 males and 28 females) who had not visited an otolaryngologist for respiratory diseases within the past three months. Participants performed sustained vowel /a/ phonation for four seconds and reading tasks with sentences ("Walk") and paragraphs ("Autumn") in a sound-treated booth. Recordings were simultaneously conducted using the four different devices and synchronized based on the CSL-recorded samples for analysis using the MDVP, ADSV, and VOXplot programs. Compared with CSL, the Cepstral Spectral Index of Dysphonia (CSIDV, CSIDS) and Acoustic Voice Quality Index (AVQI) values were lower in the WIRED and higher in the SMART. The opposite trend was observed for the L/H spectral ratios (SRV and SRS), and the WIRELESS demonstrated task-specific discrepancies. Furthermore, both the fundamental frequency (F0) and the cepstral peak prominence of the vowel samples (CPPV) had intraclass correlation coefficient (ICC) values above 0.9, indicating high reliability. These variables, F0 and CPPV were considered highly reliable for voice recordings across different microphone types. However, caution should be exercised when analyzing and interpreting variables such as the SR, CSID, and AVQI, which may be influenced by the type of microphone used.

키워드

과제정보

이 논문은 2024년도 한림대학교 교비연구비(HRF-202401-018)에 의하여 연구되었음.

참고문헌

  1. Awan, S. N., Shaikh, M. A., Awan, J. A., Abdalla, I., Lim, K. O., & Misono, S. (2023). Smartphone recordings are comparable to "Gold Standard" recordings for acoustic measurements of voice. Journal of Voice. https://doi.org/10.1016/j.jvoice.2023.01.031
  2. Awan, S. N., Shaikh, M. A., Desjardins, M., Feinstein, H., & Abbott, K. V. (2022). The effect of microphone frequency response on spectral and cepstral measures of voice: An examination of low-cost electret headset microphones. American Journal of Speech-Language Pathology, 31(2), 959-973.
  3. Castro-Tighe, S., & Inostroza-Moreno, G. (2020). Variability of microphones used for acoustic analysis of the voice in the last twenty years. Revista de Investigacion e Innovacion en Ciencias de la Salud, 2(2), 93-101.
  4. Di Cesare, M. G., Perpetuini, D., Cardone, D., & Merla, A. (2024). Assessment of voice disorders using machine learning and vocal analysis of voice samples recorded through smartphones. BioMedInformatics, 4(1), 549-565. https://doi.org/10.3390/biomedinformatics4010031
  5. Jannetts, S., Schaeffler, F., Beck, J., & Cowen, S. (2019). Assessing voice health using smartphones: Bias and random error of acoustic voice parameters captured by different smartphone types. International Journal of Language & Communication Disorders, 54(2), 292-305. https://doi.org/10.1111/1460-6984.12457
  6. Kim, H. (2012). Neurologic speech-language disorders. Seoul, Korea: Sigma Press.
  7. Kim, G. H., Lee, Y. Y., Bae, I. H., Park, H. J., & Kwon, S. B. (2018). Application of the new version of the acoustic voice quality index with Korean speakers. Communication Sciences & Disorders, 23(4), 1091-1101. https://doi.org/10.12963/csd.18556
  8. Kardous, C. A., & Shaw, P. B. (2016). Evaluation of smartphone sound measurement applications (apps) using external microphones: A follow-up study. The Journal of the Acoustical Society of America, 140(4), EL327-EL333. https://doi.org/10.1121/1.4964639
  9. Kim, G. H., von Latoszek, B. B., & Lee, Y. W. (2021). Validation of acoustic voice quality index version 3.01 and acoustic breathiness index in Korean population. Journal of Voice, 35(4), 660.E9-660.E18.
  10. Latoszek, B. B. V., Mayer, J., Watts, C. R., & Lehnert, B. (2023). Advances in clinical voice quality analysis with VOXplot. Journal of Clinical Medicine, 12(14), 4644.
  11. Lee, S. J. (2022). Current status and perspectives of telepractice in voice and speech therapy. Journal of Korean Society of Laryngology, Phoniatrics and Logopedics, 33(3), 130-141. https://doi.org/10.22469/jkslp.2022.33.3.130
  12. Lee, S. J., Choi, H. S., Kim, H. H., Byeon, H. K., Lim, S. E., & Yang, M. K. (2016). Korean version of the voice activity and participation profile (K-VAPP): A validation study. Communication Sciences & Disorders, 21(4), 695-708. https://doi.org/10.12963/csd.16348
  13. Lee, S. J., Lee, K. Y., & Choi, H. S. (2018). Clinical usefulness of voice recordings using a smartphone as a screening tool for voice disorders. Communication Sciences & Disorders, 23(4), 1065-1077. https://doi.org/10.12963/csd.18540
  14. Manfredi, C., Lebacq, J., Cantarella, G., Schoentgen, J., Orlandi, S., & DeJonckere, P. H. (2017). Smartphones offer new opportunities in clinical voice research. Journal of Voice, 31(1), 111.E1-111.E7. https://doi.org/10.1016/j.jvoice.2016.07.024
  15. McKenna, V. S., Roberts, R. M., Friedman, A. D., Shanley, S. N., & Llico, A. F. (2023). Impact of naturalistic smartphone positioning on acoustic measures of voice. The Journal of the Acoustical Society of America, 154(1), 323-333. https://doi.org/10.1121/10.0020176
  16. Parsa, V., Jamieson, D. G., & Pretty, B. R. (2001). Effects of microphone type on acoustic measures of voice. Journal of Voice, 15(3), 331-343. https://doi.org/10.1016/S0892-1997(01)00035-2
  17. Petrizzo, D., & Popolo, P. S. (2021). Smartphone use in clinical voice recording and acoustic analysis: A literature review. Journal of Voice, 35(3), 499.E23-499.E28.
  18. Svec, J. G., & Granqvist, S. (2010). Guidelines for selecting microphones for human voice production research. American Journal of Speech-Language Pathology, 19(4), 356-368. https://doi.org/10.1044/1058-0360(2010/09-0091)
  19. Sevitz, J. S., Kiefer, B. R., Huber, J. E., & Troche, M. S. (2021). Obtaining objective clinical measures during telehealth evaluations of dysarthria. American Journal of Speech-Language Pathology, 30(2), 503-516.
  20. Uloza, V., Ulozaite-Staniene, N., Petrauskas, T., Pribuisis, K., Blazauskas, T., Damasevicius, R., & Maskeliunas, R. (2023a). Reliability of universal-platform-based voice screen application in AVQI measurements captured with different smartphones. Journal of Clinical Medicine, 12(12), 4119.
  21. Uloza, V., Ulozaite-Staniene, N., Petrauskas, T., Pribuisis, K., Uloziene, I., Blazauskas, T., Damasevicius, R., ... Maskeliunas, R. (2023b). Smartphone-based voice wellness index application for dysphonia screening and assessment: Development and reliability. Journal of Voice. https://doi.org/10.1016/j.jvoice.2023.10.021
  22. Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. Scotts Valley, CA: CreateSpace.
  23. Weerathunge, H. R., Segina, R. K., Tracy, L., & Stepp, C. E. (2021). Accuracy of acoustic measures of voice via telepractice videoconferencing platforms. Journal of Speech, Language, and Hearing Research, 64(7), 2586-2599. https://doi.org/10.1044/2021_JSLHR-20-00625
  24. Yun, M. H., Lee, J. H., Lee, S. H., & Jin, S. M. (2015). Feasibility of galaxy smartphone recording as portable recorder for acoustic analysis of voice. Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics, 26(2), 104-111. https://doi.org/10.22469/jkslp.2015.26.2.104