## 5 Experiment
### Experiments on DNN-VAD  
DNN-VAD is used to reduce the waiting time for a user to receive the ASR results of what they said by triggering backtracking at the possible pause among user utterances. However, frequent backtracking at an improper time can degrade the recognition performance. Hence, in the experiments, the minimum interval between two consecutive backtracking points is set to various values. Table 9 shows the segment lengths divided by DNN-VAD and the recognition accuracy with and without DNN-VAD for test set 1. For example, when the minimum interval is limited to 6 s, an utterance is split into 3.6 segments of 8.2 s each, on average, and the word error rate (WER) is 11.13, which is slightly reduced compared to the case in which VAD is not used, where WER is 11.02.  
As the minimum interval reduces, the number of segments increases and the length of each segment increases, which means more frequent backtrackings and smaller user-perceived latencies. The accuracy degrades only slightly, which means that the backtracking point is selected reasonably. The internal investigation confirms that the segments are split mostly at the pause between phrases.  
Figure 6: Performances of an ASR system using the proposed fast parallel acoustic score computation method of BLSTM AM with the test set, in terms of (**a**) CPU and GPU usages (%) and (**b**) the number (\(k\)) of parallel decoded feature vectors of the proposed method for each acoustic score computation time. The green and blue lines indicate the CPU and GPU usages, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SylIER (\%)** & **Max. Clients** \\ \hline proposed decoding & 11.94 & 44 \\ + skip frame & 12.53 & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of SylIER (%) and the multichannel performance of the Korean online ASR systems using the proposed acoustic score computation method of BLSTM AM with/without the frame skip technique on search space expansion during Viterbi decoding, with the set set.  
To measure the waiting time from the viewpoint of users, the user-perceived latency suggested in Reference [19] is used. User-perceived latency is measured for each word uttered, and estimated empirically by measuring the difference in the timestamp of when a transcribed word is available to the user and that in an original audio. The aligned information in the recognition result is used as the timestamp of a word.  
The average user-perceived latency is 11.71 s for test set 1 without DNN-VAD, which is very large since all results are received after the end of segments are sent to the server. When DNN-VAD is applied, the average latency is reduced to 5.41 s with a minibatch of 200 frames and 3.09 s with a minibatch of 100 frames. For a detailed analysis, the histogram of latency for each word is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Use of** & **Minimum** & **Number of** & **Mean and Std. Dev.** & \\
**DNN-VAD** & **Interval(sec)** & **Segments** & **of Segment Durations(sec)** & **WER(\%)** \\ \hline No & - & 29.24 & - & 11.02 \\ Yes & 6 & 3.6 & 8.2 \(\pm\) 3.1 & 11.13 \\ Yes & 4 & 4.7 & 6.3 \(\pm\) 2.9 & 11.23 \\ Yes & 2 & 6.6 & 4.4 \(\pm\) 2.6 & 11.34 \\ Yes & 1 & 8.3 & 3.5 \(\pm\) 2.3 & 11.29 \\ \hline \end{tabular}
\end{table}
Table 9: Average number and duration of split segments and syllable error rates (%) with and without DNN-voice-activity detector (VAD).  
Figure 7: Performances of an ASR system using both the proposed fast parallel acoustic score computation method of BLSTM AM and the frame skip method, with the test set, in terms of (**a**) CPU and GPU usages (%), (**b**) number of hypothesis during Viterbi decoding, and (**c**)  the number (_k_) of parallel decoded feature vectors of the proposed method for each acoustic score computation time. The green and blue lines indicate the CPU and GPU usages, and the straight and dotted lines indicate the proposed decoding without/with frame skip.