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2. Methods- Short Term • 2.1 Actors and Stimuli o Participants: 43 mothers, (24 with some music experience, 6 with some acting experience) o Scripts: 8 conversational sentences in English: o Emotions: Anger, Happiness, Sadness, Neutral (no expression) o Subjects were recorded speaking each script while vocally portraying each unique emotion. • 8 scripts X 4 portrayals = 32 stimuli per subject • 2.2 Data Analysis o All analyses conducted in MatLab o Pitch (F0) contours and Harmonics-to-Noise Ratio (HNR) calculated through PRAAT. o Stimuli normalized to 10 seconds in length. 1. “Where is the checkbook? Its gone, I can’t find it.” 8. “I’m fixing dinner. It will take an hour.” … … 1. Introduction Parental vocalized emotions alert children to stop or change their behavior [1]. Excessive exposure specifically to anger can alter children’s neurophysiology, and increases the risk for emotional problems [2] . Acoustic Properties of Maternal Emotional Speech Peter M. Moriarty, Michelle C. Vigeant The Graduate Program in Acoustics, The Pennsylvania State University, University Park, PA 5. Conclusions and Future Work • 5.1 Acoustical Analysis o Overall trends in the LLDs support feasibility of discriminating emotion based on measured data. o Differences between script indicate an affect by language. § 5.2 Future Work o Include a calibrated measure of loudness from the stimuli recordings. o Perform utterance-level analysis, in addition to script level analysis. o Perform a Tukey means comparison and repeated measures ANOVA test to determine which LLDs differ across emotion. o Run an experiment to subjectively evaluate the quality of the emotion, and test the subjective effects of the language content. 3. PLAYBACK & SCAN 4. BRAIN ACTIVITY Long Term Goals: • Create corpus of emotional speech recordings. • Examine how emotional speech from mothers is processed by children at the neurological level • Compare acoustics and child’s brain activity. 1. RECORD STIMULI 2. ACOUSTIC ANALYSIS Short Term Goals: • Record mothers speaking with different acted emotions • Determine salient speech patterns modulated by emotion Custom MatLab Script Linux Commands PRAAT calculations 6. Acknowledgements Thank you to Martin Lawless and Matthew Neal for assistance in analysis. Work funded by the National Institute of Health (NIH) Grant # 1R21 MH104547-01 5. SUBJECTIVE RATINGS How angry does this sound? q 1 – Not angry q 2 – Less angry q 3 – More angry q 4 – Very angry X Figure 3. Waveforms (yellow) and F0 contours (green) superimposed over spectrograms of the same script spoken with four different emotions. Note the difference in waveform amplitude and F0 contour range. Waveform F0 contour Figure 1. Image from [3]. Figure 2. Diagram of the workflow of the study. Top Image from [4]. Bottom right images from [5],[6]. 7. References [1] Repacholi, B. M., & Meltzoff, A. N. Child Development, vol. 78, pp. 508-521 (2007). [2] Shackman, J. E., Shackman, A. J., & Pollak, S. D.. Emotion, vol. 7, pp. 838–852 (2007). [3] http://longbeachchildcustodyattorney.com/considerations-for-child-custody/ [4] http://www.amberusa.com/img/equipment-mri/siemens-magnetom-aera-1-5t-full.png [5] mathworks.com [6] praat.org [7] Schuller, B. et al., Speech Communication, vol. 53, no. 1, pp. 1062-1087 (2011). [8] Banse, R., Scherer, K., J. of Personality and Social Psychology. vol. 70, no. 3, pp. 617 (1996) [9] Juslin, P. N., and Laukka, P., Emotion, vol. 1, no. 4, pp. 381-412 (2001) [10] Scherer, K. R. et al., Computer Speech and Language, vol. 29, no. 1, pp 218-235 (2015) 4. Results- Summary • 4.2 Differences in LLDs across emotion o Trends in F0 Mean and STD agree with literature [7],[8]. o Jitter, Syllabic Rate, and HNR agree with [10] o Hammarberg agrees with [8], HF500 with [9] o 1- way ANOVA test grouped by emotion significant p < .001 for all LLDs Figure 4. This is a plot of the mean of each LLD taken over subject, normalized to zero mean and unit variance. Bars are grouped by emotion labeled on the horizontal axis. Differences between values within emotion category suggest an affect caused by the language content of the script. Figure 5. This is a plot of the mean values of each LLD taken over script and subject. 4. Results- Script Comparison • 4.1 Low-Level Descriptors (LLD) Considered o F0: pitch or glottal pulse rate of voiced sections [Hz] o Jitter: average difference in glottal periods [s] o Shimmer: average short-time difference in intensity [dB] o HNR: Ratio of periodic energy to energy of noise [dB] o Syllabic Rate: Syllables per second [S/s] o Hammarberg Index: Peak energy (0-2kHz) / (2kHz-5kHz) [dB] o HF500: (Energy > 500 Hz)/ (Energy < 500 Hz) o Spectral COG [Hz]: Weighted mean frequency of spectrum. “chk-b”
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