# Within and Across-Language Comparison of Vocal Emotions in Mandarin and English
## 2 Methods
### Measurements  
Automatic segmentation of recordings was made using SPPAS [33]. Three tiers (phoneme tier, syllable tier and sentence tier) were generated and manually corrected afterward. A sequence of pitch target points was detected by Momel algorithm [34] using Praat. Pitch contours were modelled as continuous smooth curves, interpolated quadratically from pitch target points of each utterance in order to eliminate microprosodic effects. Based on the Momel outputs, a set of prosodic cues was measured to quantify the degree of pitch movements as a function of time.  
Seven prosodic cues were generated by a Praat script: (1) Number of syllables per second (Speech Rate); (2) Mean intensity of each sentence (Mean Intensity); (3) Mean pitch of each sentence (Mean Pitch); (4) Pitch range of each sentence (Pitch range) (5) Average absolute difference between two adjacent pitch target points divided by distance in seconds (Mean Absolute Slope, MAS hereafter), reflecting the frequency of pitch movements [35]; (6) Average pitch difference between two adjacent pitch target points for rise and fall separately in each sentence (Rise, Fall), which determine the degree of pitch raising and pitch falling [35]; and (7) Average slope for rise and fall separately in each sentence (Rise Slope, Fall Slope), indicating the speed rate of pitch raising and pitch falling [35]. These cues portrayedboth global and local pitch movements of each sentence. Pitch-related cues were normalized to eliminate individual differences using the OMe (Octave-Median) scale [36] by applying the following equation:  
\[\text{OMe}=\text{log}_{2}\left(\text{Hz}/\text{Median}\right)\]  
where Hz is a raw pitch value and Median indicates the median of a speaker's pitch range.  
In addition, we obtained a set of spectral cues using VoiceSauce [37] through the voicing parts of each sentence. They are: (1) Cepstral Peak Prominence (CPP), indicating harmonics-to-noise ratio and periodicity [38]; (2) Amplitudes of the first, second and fourth harmonics (H1, H2 and H4); (3) Amplitude difference between the first and second harmonics (H1-H2), reflecting the relative breathiness or ceaikiness of phonation [39]; and (4) Amplitude difference between the first harmonic and harmonics nearest to F1, F2 and F3 (H1-A1, H1-A2 and H1-A3), as measures of spectral tilt [30]. This set of spectral cues was often used as the standard in measuring different phonation properties [40].  
Next, three kinds of EGG cues were extracted using EggWorks [41], including (1) Contact quotient (CQ), illustrating the duration of the vocal fold contact during one vocal fold period [42]; (2) Peak Increase in Contact (PIC), the peak positive value in the EGG derivative, indicating the highest speed of vocal fold contact [43]; and (3) Speed Quotient (SQ), the ratio between closing duration and opening duration, reflecting the asymmetry of the EGG pulses [44]. These cues are indicators of the physiological mechanism of vocal fold vibration during speech production.  
In summary, in order to assess the vocal expression of emotion in Mandarin and English, we measured a total of 20 cues which were then converted to z-scores combining all the emotions separately for each speaker.