## 4 Experiments and Results
### Results
#### 4.2.2 The Accuracy of Speech Recognition at the End of Tracking  
For the second challenge in the cocktail party problem, the clarity of the target audio can be reflected by the accuracy of speech recognition. To verify the effectiveness of our motion module, the WER was calculated at the end of tracking. Two strategies were applied to control the robot to track the target, the robot went directly to the target or the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Success Rate \(\uparrow\)** & **Distance to Target \(\downarrow\)** \\ \hline SEVD-MUSIC [18] (random) & 24\% & 2.34 m \\ SEVD-MUSIC [18] (high response) & 40\% & 2.02 m \\ GSVD-MUSIC [19] (random) & 28\% & 2.28 m \\ GSVD-MUSIC [19] (high response) & 42\% & 1.96 m \\ SELDnet [20, 21] & 62\% & 1.68 m \\ AV-SSLT (ours) & 82\% & 1.28 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance on locating the sound of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sampling Rate & 16,000 Hz \\ Frame Length & 512 \\ Window Size & 64 \\ Dimension of Mel-spectrograms & 13 \\ Lower Bound Frequency & 300 Hz \\ Higher Bound Frequency & 2800 Hz \\ Max Number of Sounds & 3 \\ Interval of Azimuth & 10\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter list of the MUSIC-based algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sampling Rate & 16,000 Hz \\ Frame Length & 512 \\ Window Size & 64 \\ Dimension of Mel-spectrograms & 13 \\ Lower Bound Frequency & 300 Hz \\ Higher Bound Frequency & 2800 Hz \\ Max Number of Sounds & 3 \\ Interval of Azimuth & 10\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter list of the SELDnet algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Success Rate \(\uparrow\)** & **Distance to Target \(\downarrow\)** \\ \hline SEVD-MUSIC [18] (random) & 24\% & 2.34 m \\ SEVD-MUSIC [18] (high response) & 40\% & 2.02 m \\ GSVD-MUSIC [19] (random) & 28\% & 2.28 m \\ GSVD-MUSIC [19] (high response) & 42\% & 1.96 m \\ SELDnet [20, 21] & 62\% & 1.68 m \\ AV-SSLT (ours) & 82\% & 1.28 m \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average response time of each module.  
robot moved relying on the motion module. Figure 6 shows the trajectories of the robot in two different episode settings, one with the target speaker outside of the interference sources and the other with the target speaker in the middle of the interference sources. It can be seen that the motion module can control the robot to move it toward the target while keeping away from other interfering sound sources in the surroundings. Table 6 reports the average WERs for these two strategies. Obviously, the WER is lower when the robot was guided by our motion module, implying that AV-SSLT can improve the quality of captured speech audio under the control of the motion module.  
Figure 6: The trajectories of the robot in two different episodes. (**i**) The trajectory of the robot moving under the control of the motion module and (**ii**) the trajectory of the robot moving directly towards the sound source. The yellow circle indicates the target speaker and the red circles represent the interfering sound sources. (**a**) The trajectories of the robot in the situation where the target speaker was on the outside of the interfering sound sources. (**b**) The trajectories of the robot in the situation where the target speaker was in the middle of the interfering sound sources.