## 5 Evaluation Results and Discussion
### Objective Evaluation
#### 5.1.3 Measuring Speech Quality after Analysis and Re-Synthesis  
It is well-known that efficient methods for evaluating speech quality are typically based on subjective listening tests. However, there are various issues related to the use of subjective testing. It can sometimes be very expensive, time-consuming, and hard to find a sufficient number of suitable volunteers [65; 66]. For that reason, it can often be useful in this work to run objective tests in addition to listening tests. A range of objective speech quality and intelligibility measures are considered to evaluate the quality of synthesized speech based on the modified version of our continuous vocoder:  
* One objective measure is the Weighted-Slope Spectral Distance (WSS) [67], which computes the weighted difference between the spectral slopes in each frequency band. The spectral slope is found as the difference between adjacent spectral magnitudes in decibels. * As the speech production process can be modeled efficiently with Linear Predictive Coefficients (LPC), another objective measure is called the Log-Likelihood Ratio (LLR) [65]. It is generally a distance measure that can be directly calculated from the LPC vector of the clean and enhanced speech. The segmental LLR values were limited in the range of [0, 1]. * We also adopt the frequency-weighted segmental SNR (fwSNRseg) for the error criterion to measure speech quality, since it is said to be much more correlated with subjective speech quality than classical SNR [68]. The fwSNRseg measure applies weights taken from the ANSI SII standard to each frequency band [69]. Instead of working on the entire signal, only frames with segmental SNR in the range of -10 to 35 dB were considered in the average. * Moreover, [PERSON] and [PERSON] introduced an effective objective measure, which they called the Extended Short-Time Objective Intelligibility (ESTOI) measure [69]. The ESTOI calculates the correlation between the temporal envelopes of clean and enhanced speech in short frame segments. * The final objective measure used here is the Normalized Covariance Metric (NCM) [70], which is based on the covariance between the clean and processed Hilbert envelope signals. Before we proceed to further detail on examining the results, we will first describe our experiments. Several experiments based on the HNR parameter were implemented to find out the best continuous pitch algorithm that works well with our continuous vocoder, as well as to understand the behavior of adding a new HNR excitation parameter in both voiced/unvoiced speech frames. The three experiments are summarized in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**GPE \%**} & \multicolumn{3}{c}{**MFPE**} & \multicolumn{3}{c}{**STD**} \\  & **BDL** & **JMK** & **SLT** & **BDL** & **JMK** & **SLT** & **BDL** & **JMK** & **SLT** \\ \hline baseline & 33.170 & 40.057 & 27.502 & 4.050 & 3.901 & 3.512 & 4.393 & 4.293 & 3.912 \\ contF0\_AKF & 31.728 & 40.865 & 26.122 & 3.211 & 3.241 & 2.898 & 3.465 & 3.627 & 3.448 \\ contF0\_TWRP & 29.464 & 37.839 & 26.932 & 3.199 & 3.165 & 2.890 & 3.449 & 3.511 & 3.186 \\ contF0\_STMSK & 31.418 & 37.052 & 26.352 & 2.128 & 1.896 & 2.067 & 2.103 & 1.658 & 2.058 \\ \hline YANGaf & 27.530 & 35.200 & 25.852 & 2.233 & 2.181 & 2.175 & 2.206 & 2.219 & 2.265 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average performance score per each speaker in the presence of additive white noise (SNR = 0 dB).