# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 6 Discussion & Conclusions  
In the current study, we examine the use of prosody-based features for severity assessment of dysarthric speech for both English and Korean datasets. According to the results from machine learning experiments, higher accuracy is achieved when using prosody-based measures in comparison to MFCC or voice quality features. In particular, prosody is effective for improving the classification of mild dysarthria. This is promising in showing that even mild impairments in speech can be automatically detected. Our findings support and further generalize the results from Reference [29]. The best performance with the TORGO dataset occurs when training on both MFCCs and prosody-based features. An accuracy of 75.63% is obtained using a neural network classifier, which is a relative accuracy increase of 18.13% in comparison to solely using baseline MFCC features (64.02% accuracy). For the QoLT dataset, a random forest classifier trained on all acoustic features leads to the highest accuracy of 70.10%, which is a relative accuracy increase of 16.83% compared to solely training on MFCCs (60.00% accuracy). Furthermore, the classification of mild dysarthric utterances is improved when including prosody-based features. For English, this leads to a recall improvement from 42.42% to 83.34% for mild utterances, and for Korean, a recall increase from 36.73% to 80.00%. Overall accuracy (75.63% for English and 70.10% for Korean) for both datasets is lower than results from previous studies, but important differences may explain why this may be the case. In Reference [36], researchers also use prosody-based features for severity assessment and reach an accuracy of 93%, but as previously mentioned, the database used in the study only contains male speakers, only one healthy speaker, and only one type of non-sense carrier sentence. To increase generalizability, our study uses a different database that contains several utterances produced by multiple males, females, healthy, and dysarthric speakers. Aside from the study in Reference [38], no other study examines the use of prosody-based measures for severity assessment with the TORGO dataset. However, there are studies using other acoustic features. An accuracy of 98.7% was achieved in Reference [62], using timbre-based features, but this study does not include healthy speakers. Furthermore, isolated words are also considered in their dataset, whereas we only considered continuous speech. Similarly, the study in Reference [63] uses MFCCs along with delta features in a CNN classifier with the TORGO dataset and reaches a severity assessment accuracy of 98.3%. However, this study also excludes healthy speech data and only conducts testing on two speakers (one mild and one severe), whereas the current study examines all speakers for testing. In a clinical setting, it would be helpful to not only assess dysarthria but also give diagnostics of specific speech impairments. While spectral information can help detect dysarthria, they can be difficult to interpret and, in some cases such as MFCCs, remove prosodic information. By including prosody-based features, clinicians can better interpret what aspects of speech are impaired and prepare \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy \%** & **Accuracy \%** & **Accuracy \%** & **Accuracy \%** \\
**MFCC + Prosody** & **MFCC + Voice Quality** & **Prosody + Voice Quality** & **All Acoustic Features** \\ \hline RF & 66.73 & 64.11 & 60.73 & 70.10 \\ SVM & 65.78 & 66.67 & 64.11 & 67.52 \\ NN & 56.40 & 54.72 & 57.78 & 61.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Accuracy results for various acoustic feature combinations.