International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 15, No. 4, 8 Aug. 2023
Cover page and Table of Contents: PDF (size: 474KB)
An Experimental and Statistical Analysis to Assess impact of Regional Accent on Distress Non-linguistic Scream of Young Women
Full Text (PDF, 474KB), PP.33-43
Views: 0 Downloads: 0
Author(s)
Disha Handa
1
Renu Vig
2
Mukesh Kumar
3,*
Namarta Vij
4
Index Terms
Speech, Regional accent, screaming, women scream, correlation, statistical approach, Acoustic features
Abstract
Scream is recognized as constant and ear-splitting non-linguistic verbal communication that has no phonological structure. This research is based on the study to assess the effect of regional accent on distress screams of women of a very specific age group. The primary goal of this research is to identify the components of non-speech sound so that the region of origin of the speaker can be determined. Furthermore, this research can aid in the development of security techniques based on emotions to prevent and report criminal activities where victims used to yell for help. For the time being, we have limited the study to women because women are the primary victims of all types of criminal’s activities. The Non-Speech corpus has been used to explore different parameters of scream samples collected from three different regions by using high-reliability audio recordings. The detailed investigation is based on the vocal characteristics of female speakers. Further, the investigations have been verified with bi-variate, partial correlation and one-way ANOVA to find out the impact of region-based accent non-speech distress signal. Results from the correlation techniques indicate that out of four attributes only jitter varies with respect to the specific region. Whereas ANOVA depicts that there is no significant regional impact on distress non-speech signals.
Cite This Paper
Disha Handa, Renu Vig, Mukesh Kumar, Namarta Vij, "An Experimental and Statistical Analysis to Assess impact of Regional Accent on Distress Non-linguistic Scream of Young Women", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.4, pp. 33-43, 2023. DOI:10.5815/ijigsp.2023.04.03
Reference
[1]Arnal, L. H., Flinker, A., Kleinschmidt, A., Giraud, A. L., & Poeppel, D. (2015). Human screams occupy a privileged niche in the communication soundscape. Current Biology, 25(15), 2051-2056.
[2]Handa, D., & Vig, R. (2020, February). Distress Screaming vs Joyful Screaming: An Experimental Analysis on Both the High Pitch Acoustic Signals to Trace Differences and Similarities. In 2020 Indo–Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN) (pp. 190-193). IEEE.
[3]Huang, W., Chiew, T. K., Li, H., Kok, T. S., & Biswas, J. (2010, June). Scream detection for home applications. In 2010 5th IEEE Conference on Industrial Electronics and Applications (pp. 2115-2120). IEEE.
[4]Liao, W. H., & Lin, Y. K. (2009, October). Classification of non-speech human sounds: Feature selection and snoring sound analysis. In 2009 IEEE International Conference on Systems, Man and Cybernetics (pp. 2695-2700). IEEE.
[5]Mak, M. W., & Kung, S. Y. (2012, March). Low-power SVM classifiers for sound event classification on mobile devices. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1985-1988). IEEE.
[6]Green, J. A., Whitney, P. G., & Gustafson, G. E. (2010). Vocal expressions of anger. In International handbook of anger (pp. 139-156). Springer, New York, NY.
[7]Vlasenko, B., Philippou-Hübner, D., Prylipko, D., Böck, R., Siegert, I., & Wendemuth, A. (2011, July). Vowels formants analysis allows straightforward detection of high arousal emotions. In 2011 IEEE International Conference on Multimedia and Expo (pp. 1-6). IEEE.
[8]Pisanski, K., Raine, J., & Reby, D. (2020). Individual differences in human voice pitch are preserved from speech to screams, roars and pain cries. Royal Society open science, 7(2), 191642.
[9]Anikin, A., & Persson, T. (2017). Nonlinguistic vocalizations from online amateur videos for emotion research: A validated corpus. Behavior research methods, 49(2), 758-771.
[10]D'Antonio, L. L., Scherer, N. J., Miller, L. L., Kalbfleisch, J. H., & Bartley, J. A. (2001). Analysis of speech characteristics in children with velocardiofacial syndrome (VCFS) and children with phenotypic overlap without VCFS. The Cleft palate-craniofacial journal, 38(5), 455-467.
[11]Coppens-Hofman, M. C., Terband, H., Snik, A. F., & Maassen, B. A. (2016). Speech characteristics and intelligibility in adults with mild and moderate intellectual disabilities. Folia Phoniatrica et Logopaedica, 68(4), 175-182.
[12]Derakhshandeh, F., Nikmaram, M., Hosseinabad, H. H., Memarzadeh, M., Taheri, M., Omrani, M., ... & Sell, D. (2016). Speech characteristics after articulation therapy in children with cleft palate and velopharyngeal dysfunction–A single case experimental design. International journal of pediatric otorhinolaryngology, 86, 104-113.
[13]Wolk, L., Abdelli-Beruh, N. B., & Slavin, D. (2012). Habitual use of vocal fry in young adult female speakers. Journal of Voice, 26(3), e111-e116.
[14]Hansen, J. H., Nandwana, M. K., & Shokouhi, N. (2017). Analysis of human scream and its impact on text-independent speaker verification. The Journal of the Acoustical Society of America, 141(4), 2957-2967.
[15]Begault, D. R. (2008, June). Forensic analysis of the audibility of female screams. In Audio Engineering Society Conference: 33rd International Conference: Audio Forensics-Theory and Practice. Audio Engineering Society.
[16]Hansen, J. H., Nandwana, M. K., & Shokouhi, N. (2017). Analysis of human scream and its impact on text-independent speaker verification. The Journal of the Acoustical Society of America, 141(4), 2957-2967.
[17]Nandwana, M. K., & Hansen, J. H. (2014). Analysis and identification of human scream: Implications for speaker recognition. In Fifteenth Annual Conference of the International Speech Communication Association.
[18]Cooper, W. E., & Sorensen, J. M. (2012). Fundamental frequency in sentence production. Springer Science & Business Media.
[19]Almaadeed, N., Aggoun, A., & Amira, A. (2016). Text-independent speaker identification using vowel formants. Journal of Signal Processing Systems, 82(3), 345-356.
[20]Boersma, P. (2014). The use of Praat in corpus research. The Oxford handbook of corpus phonology, 342-360.
[21]Janjua, Z. H., Vecchio, M., Antonini, M., & Antonelli, F. (2019). IRESE: An intelligent rare-event detection system using unsupervised learning on the IoT edge. Engineering Applications of Artificial Intelligence, 84, 41-50.
[22]Schwartz, J. W., Engelberg, J. W., & Gouzoules, H. (2020). Was that a scream? Listener agreement and major distinguishing acoustic features. Journal of Nonverbal Behavior, 44(2), 233-252.
[23]Ward, L., Shirley, B. G., Tang, Y., & Davies, W. J. (2017, August). The effect of situation-speciļ¬c non-speech acoustic cues on the intelligibility of speech in noise. In INTERSPEECH 2017, 18th Annual Conference of the International Speech Communication Association.
[24]Hurring, G., Hay, J., Drager, K., Podlubny, R., Manhire, L., & Ellis, A. (2022). Social Priming in Speech Perception: Revisiting Kangaroo/Kiwi Priming in New Zealand English. Brain Sciences, 12(6), 684.
[25]O'Donovan, R., Sezgin, E., Bambach, S., Butter, E., & Lin, S. (2020). Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning. JMIR Formative Research, 4(6), e18279.
[26]Mukesh Kumar, Nidhi, Bhisham Sharma, Disha Handa, "Building Predictive Model by Using Data Mining and Feature Selection Techniques on Academic Dataset", International Journal of Modern Education and Computer Science, Vol.14, No.4, pp. 16-29, 2022.
[27]Mukesh Kumar, Nidhi, Anas Quteishat, Ahmed Qtaishat, "Performance Comparison of the Optimized Ensemble Model with Existing Classifier Models", International Journal of Modern Education and Computer Science, Vol.14, No.3, pp. 76-87, 2022.
[28]Shriram D. Raut, Vikas T. Humbe,"Statistical Analysis of Resulting Palm vein Image through Enhancement Operations", International Journal of Information Engineering and Electronic Business, vol.5, no.6, pp.47-54, 2013.
[29]Paschal A. Ochang, Philip J. Irving, Paulinus O. Ofem,"Research on Wireless Network Security Awareness of Average Users", International Journal of Wireless and Microwave Technologies, Vol.6, No.2, pp.21-29, 2016.