# Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications
## 2 Human-Voice Interface System
### Automatic Speech Recognition (ASR)
#### 2.3.4 Model Testing Result  
In this section, the training results of the two models are presented: (1) the SDR with speaker separation model and (2) the WER with speech recognition mode. (1) Speaker Separation Model Testing  
There are different training sets for evaluating the performance of the speaker separation model. Table 3 shows the results of the different training sets (i.e., train-clean-100, train-clean-360, train-clean-100, and 8 train-clean-360). In the first experiment, the performance results of the train-clean-100 dataset with 251 speakers were not good with an SDR of only 3.2 dB. Further, the actual output audio was much different from the clean audio. Moreover, the output volume was about 80% less than the input volume and the specific speaker audio was not separated very successfully, and is similar to turning down the volume of the mixed audio. The training effect of the train-clean-360 with 921 speakers was slightly improved; the volume was still cut, but the magnitude was reduced and the separation effect was slightly present. As can be seen in Figure 13, the SDR was raised to 5.4 dB, and the volume of speaker A was greater than the volume of speaker B. Finally, the two training sets were summed up and the number of speakers reached about 1200. The training was conducted twice. The first time we set the batch size to 8, the SDR stopped at about 7.5 dB at the frequency step of 300 k; so, we lowered the batch size to 4, and the SDR continued to set at 300 k and approached a better SDR of 8.6 dB at 530 k. As shown in Figure 14, the separated clean audio was precisely matched to the actual output. The proposed effectiveness of the separated model was to reduce the influence of the outside environment. This experiment also illustrated that the more speakers are included in the training set, the better the performance of the separated trained model. This characteristic of the speaker separation model also supports the next ASR model to obtain the adaptive reality to fit the right voice command in a real environment. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Training Set** & **SDR** & **Frequency (Step)** & **Batch Size** \\ \hline Train-100 & 3.2 dB & 65 k & 8 \\ Train-360 & 5.4 dB & 65 k & 8 \\ Train-100 and train-360 & 7.5 dB & 30 k & 8 \\ Train-100 and train-360 & 8.6 dB & 530 k & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of speaker separation model. Evaluations for Automatic Speech Recognition Model  
The model discussed earlier was added to the ASR system. The training data were divided into several different sets to evaluate the goodness of WER. All the training cycles were set as epoch = 100 and batch size = 4. The audio was added to the noise to obtain the adaption of the speaker separation. The test set with a different amount of training dataset was mainly applied to evaluate the ASR model (see Table 4). When the total training cycle for train-100 audio data was finished, it obtained a not-so-good WER of 14%. In another experiment, the total training cycle was gradually added to the other training data set (i.e., train-360 and train-500) to approach the best WER, which was from 14% to 5.3%. The smallest unit in the English language is a single word, so most of the Speech Recognition model uses a WER index to detect the performance of the trained result. In this paper's real speech recognition experiment, a total of 129 different English voice patterns were continuously collected from different individuals and the pre-trained speech recognition model built in this study was applied to finally approach the correct rate of 89.3% in the real environment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Training Set** & **WER Test Result** \\ \hline Train-100 & 14.1\% \\ Train-360 & 11.5\% \\ Train-100 and train-360 & 10.3\% \\ Train-100 and train-360, and train-500 & 5.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluations of automatic speech recognition model. Figure 14: The related SDR value with the amount dataset of 1200 people.