# Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering
## 6 Results
### The Impact of Data Augmentation Strategies
As can be seen from Table 6, statistically significant improvement (\(p<0.05\)) in the accuracy for the HMM recognizer is obtained only in the OWP case for TST, VC, and TST \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Recognizer**} & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Scenario** \\ \end{tabular} } & \multicolumn{6}{c}{**Augmentation**} \\  & & **PS** & **TST** & **VC** & **PS + TST** & **PS + VC** & **TST + VC** & **PS + TST + VC** \\ \hline \multirow{3}{*}{HMM} & OW & 1.000 & 0.999 & 1.000 & 1.000 & 1.000 & 0.903 & 1.000 \\  & OP & 0.998 & 1.000 & 0.985 & 0.999 & 0.998 & 1.000 & 1.000 \\  & OWP & 0.811 & **0.007** & **0.002** & 0.968 & 0.957 & **0.001** & 0.804 \\  & OW & **0.010** & 0.122 & **0.027** & **0.006** & **0.021** & 0.097 & **0.004** \\ \cline{1-1}  & OP & **0.018** & **0.017** & **0.002** & **0.016** & **0.007** & **0.049** & **0.018** \\ \cline{1-1}  & OWP & **0.001** & **0.001** & **0.001** & **0.001** & **0.001** & **0.002** & **0.001** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The \(p\)-value calculated for hypothesis H\({}_{\text{A}}\) in relation to seven augmentation strategies (OW—original + whisper; OP—original + pseudo-whisper; OWP—original + whisper + pseudo-whisper). The statistically significant \(p\)-values are bolded. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Scenario** \\ \end{tabular} } & \multirow{2}{*}{**PS**} & \multicolumn{6}{c}{**Augmentation**} \\  & & **TST** & **VC** & **PS + TST** & **PS + VC** & **TST + VC** & **PS + TST + VC** \\ \hline W & 84.29 & 83.72 & 83.88 & 84.22 & 84.37 & 83.70 & 84.93 \\ P & 69.52 & 69.76 & 69.23 & 69.94 & 69.00 & 69.72 & 68.85 \\ OW & **85.82** & 84.98 & **85.18** & **85.62** & **85.84** & 85.02 & **85.80** \\ OP & **85.73** & **85.80** & **86.17** & **86.10** & **85.74** & **85.16** & **85.80** \\ OWP & **87.54** & **87.37** & **87.28** & **87.97** & **88.06** & **86.86** & **87.31** \\ ORIGINAL & & & & & 84.14 & & \\ (NO AUG) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average recognition accuracy (DBtest database) for seven data augmentation techniques and CNN framework. The accuracies with statistically significant improvements are bolded. + VC augmentation strategies. Statistically significant improvement is obtained for all techniques in the OWP and OP cases for the CNN recognizer. Processing time is a crucial metric for evaluating the efficiency during the training phase and real-time processing capabilities in the decoding phase of ASR systems. The Real-Time Factor (RTF) measures the speed of ASR during decoding, defined as the ratio between the processing time and the input duration. Figure 12 shows the training time and RTF for the best augmentation strategies (VC for HMM and PS + VC for CNN). The results obtained suggest the following conclusions:  
* Expanding the training dataset by incorporating augmented utterances significantly increases the training time but improves accuracy, particularly for the CNN recognizer. * The RTF for the HMM recognizer is considerably higher and remains relatively stable, even with the expansion of the training dataset.