## 5 Experiments and Results
### Experiment III: Multilingual Shared Features Ranking and Analysis  
One of the advantages of the MTL is that it can exploit shared low-dimensional representations in multiple related tasks to improve the performance of all or some of the tasks. In this study, the MTL learns MSFs in language recognition and emotion recognition to enhance SER. To further investigate the similarities and differences between the MSFs learned from the MSFL and the monolingual feature representations learned from MSFL_ST, we first obtain the feature weights from the attention mechanism layer. Secondly, single-task experiments with monolinguals are implemented to get the feature weights for each language from the MSFL_ST. Then, the features are sorted according to the attention weights in descending order and we compared the top 30 MSFs with the top 30 EMO-DB features (EFs), top 30 CASIA features (CFs), and top 30 SAVEE features (SFs) learned from MSFL_ST. Due to the limited space, we list the top 10 features in MSFs, EFs, CFs, and SFs in Table 7. The procedure of feature ranking and comparison is shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Ranking} & \multicolumn{2}{c}{**Multilingual**} & **EMO-DB** & **CASIA** & **SAVEE** \\  & **Shared Features (MSFs)** & **Features (EFs)** & **Features (CFs)** & **Features (SFs)** \\ \hline \multirow{2}{*}{1} & **slopeV0-500\_** & HNRdBACF\_** & spectralFlux\_** & F0 semitoneFrom27.5 Hz\_** \\  & **sma3 nz\_**,_amen** & sma3 nz\_**,_amen** & sma3 nz\_**,_amen** & sma3 nz\_**,_pctrangeo-2** \\  & **F0 semitoneFrom27.5 Hz\_** & slopeV0-500\_** & slopeV500-1500\_** & F0 semitoneFrom27.5 Hz\_** \\
2 & **sma3 nz\_** & sma3 nz\_**,_amen** & sma3 nz\_**,_amen** & sma3 nz\_**,_amen** \\  & **pctrangeo-2** & sloepUV500-1500\_** & sloepUV500-1500\_** & slopeV500-1500\_** \\  & **smeen** & SegmentLength & **sma3 nz\_**,_amen** & sma3 nz\_**,_amen** \\
4 & **HNRdBACF\_** & logRelF0-H1-H2\_** & F2 bandwith & F0 semitonefrom27.5 Hz\_**,_sma3 nz\_** \\  & **sma3 nz\_**,_amen** & sma3 nz\_**,_amen** & percentile80.0 \\  & **spectralFlux\_** & Equivalent & hammarbergIndexV & F2 bandwidth\_**,_sma3 nz\_** \\
5 & **sma3\_** & SoundLevel\_dBp** & _sma3 nz\_**,_amen** & stddevNorm \\  & **stddevNorm** & **loudness\_sma3\_** & hammarbergIndexV\_** & mfc1V\_sma3 nz\_**,_amen** \\
6 & **percentile20.0** & sma3 nz\_**,_amen** & mfc1V\_**,_sma3 nz\_**,_amen** & slopeV0-500\_**,_sma3 nz\_**,_amen** \\  & **spectralFluxUV\_** & HNRdBACF\_** & slopeUV0-** & F0 semitoneFrom27.5 Hz\_** \\
7 & sma3 nz\_**,_amen** & sma3 nz\_**,_stddevNorm** & 500\_**,_sma3 nz\_**,_amen** & _sma3 nz\_**,_meanRisingSlope \\  & **loudnessPeaks** & mfcc3\_sma3\_** & F1 frequency\_**sma3 nz\_** & StddevUnvoiced \\  & **PerSec** & stddevNorm & stddevNorm & SegmentLength \\  & **loudness\_sma3\_** & F0 semitoneFrom27.5 Hz\_** & loudness\_sma3\_** & spectralFluxV\_sma3 nz\_** \\
9 & stddev & sma3 nz\_pctrangeo-2** & percentile20.0 & stddevNorm \\  & **F3 bandwidth\_sma3 nz\_**amen** & F2 frequency\_** & hammarbergIndexV\_** & F0 semitoneFrom27.5 Hz \\  & & sma3 nz\_stddevNorm** & sma3 nz\_stddevNorm** & _sma3 nz\_stddevRisingSlope \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Feature ranking based on attention weights. Figure 6: The confusion matrix of the SAVEE corpus. (**a**) The confusion matrix for the MSFL_ST model. (**b**) The confusion matrix for the MTL_DNN model. (**c**) The confusion matrix for the MSFL model. As shown in Table 7, the LLDs in the top 10 HSFs of the three monolingual corpora (i.e., EFs, CFs, and SFs) are as follows:  
1. EMO-DB: HNR, Spectral Slope 0-500 Hz, Harmonic difference H1-H2, Hammarberg index, MFCC-3, F0 Pitch, and Formant-2 frequency;
2. CASIA: Spectral Flux, Spectral Slope 0-500 Hz and 500-1500 Hz, Formant-2 bandwidth, Hammarberg index, MFCC-1, Formant-1 frequency, and Loudness;
3. SAVEE: F0 Pitch, Spectral Slope 0-500 Hz and 500-1500 Hz, Formant-2 bandwidth, Spectral flux.