## 5 Experiment
### Experiments on the Proposed Fast Parallel Acoustic Score Computation Method  
The proposed fast parallel acoustic score computation method can be utilized by replacing the baseline acoustic score computation method with no AM changes in the baseline Korean ASR system. As shown in Table 7, the ASR system using the proposed method of Section 3 achieves the same SylIER when compared to the system using the baseline method of Section 2.3. No performance degradation can be obtained since we only modify the way how the acoustic scores are calculated by accelerating a GPU parallelization. Moreover, the ASR system using the proposed method supports 22 more concurrent clients for the test set, when compared to the system using the baseline method. Therefore, we conclude that the proposed acoustic score computation method increases the concurrent clients with no performance degradation.  
To analyze the effects of the proposed acoustic score computation method, we compare the CPU and GPU usages (%) of ASR systems using the baseline and proposed methods for the test set. The averaged usages of the CPU and GPU are 78.58% and 68.17%, respectively, when the proposed method is used. By comparing Figure 5b and Figure 6a, the averaged usages of the CPU and GPU are improved by 51.24% and 7.90%, respectively. It can be concluded that the proposed method reduces the processing time of GPU and the waiting time of CPU by reducing the transfer overhead and increasing the GPU parallelization. In addition, we examine the number (\(k\)) of parallel decoded feature vectors at each time stamp when using the proposed method, as shown in Figure 6b. The parallel decoded feature vectors varies from 2 to 26, which is depend on the subsequent step, an optimal path search of Viterbi decoding.  
For further improvement in the multichannel performance, some optimization methods can be applied, such as beam pruning. In this study, we apply a simple frame skip method during a token passing-based Viterbi decoding. That is, a token propagation is only performed in the odd time stamps during Viterbi decoding. From the experiments of the proposed acoustic score computation, the ASR system combined with the frame skip method supports up to 59 concurrent clients, although the SylIERs are degraded by 4.94% for the set, when compared to the ASR system without the frame skip method, as shown in Table 8.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **SylIER (\%)** & **Max. Clients** \\ \hline baseline & 11.94 & 22 \\ proposed & 11.94 & 44 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of SylIER (%) and the multichannel performance of the Korean online ASR systems using the baseline and proposed acoustic score computation methods of BLSTM AM, with the test set.  
Figure 5: Comparison of CPU and GPU usages (%) of ASR systems using the baseline acoustic score computation method for (**a**) LSTM-based AM and (**b**) BLSTM AM with the test set. The green and blue lines indicate the CPU and GPU usages.  
Again, we measure the CPU and GPU usages (%) of the ASR systems employing the proposed method with/without the frame skip method with the test set, as shown in Figure 7a. The averaged usages of the CPU and GPU are measured as 47.24% and 71.87%. Note that the frame skip method unburdens the CPU load, and thus, the GPU usage is accordingly improved. Moreover, Figure 7b compares the number of active hypotheses during Viterbi decoding.  
In addition, we examine the number (\(k\)) of parallel decoded feature vectors at each time stamp when using the proposed method, as shown in Figure 7c. The parallel decoded feature vectors varies from 22 to 35. When compared to the Figure 6b, the parallel decoded vectors are increased due to the reduced computation during Viterbi decoding when combining the frame skip method.