# 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
Figure 13: The related SDR value with the amount dataset of 921 people. [PERSON] et al. used the semi-supervised learning method with the large-scale YouTube-based data to pre-train the conformer model based on the original LibriSpeech database and the upstream/downstream to self-train the model size, so it will match the multiple public datasets. Experiential results reached the best WER of 7.7% as it approached multiple tasks by voice command [34]. Several more studies have registered and trained the deep neural network HMM to advance ASR in a real environment. A pretraining ASR system has been built with a novel learning stratagem and with a structure of the artificial neural network (ANN) model based on a large, but limited playback loudspeaker sub databases. It was aimed to outperform the famous and appropriated Google API, IBM API, and Bing API in terms of the WER [35] after completing an environment-based training cycle with the specific questionnaires. In another voice command with robot application study [36], researchers provided a cloud-based NLP platform and ROS-based mobile robot system for participants interacting with the robot. The lowest WER it obtained was 5.8% and the highest was 83.3% with the correct rate of voice entity detections in an office, without an ambient noise environment. The WER increased to 21.9% and the correct rate of entity detections was decreased to 61.7% with respect to the effect of noise. In the research paper [37], receiving and recognizing voice commands were presented using a LabVIEW machine learning model for a person to communicate with the robot. Its practical tests, voice command reached an average precision of 86% for \"go forward and back\", and 78% correctness for \"left and right\" to control the robot's actions. Its total average precision reached 82%, while people used voice to control the mobile robot forward and backward. A multi-stream HMM decision fusion [38] was also used to improve the voice recognition rate of the acoustic model to 88.5% in the close-talking type of ASR experiments. The other identification of voice correctness was approximately 72.4% when the throat microphone was used to test the same voice command. Table 5 compares the evaluations of WER and speech recognition accuracy rates in many different methods. This data analysis illustrates that the proposed deep-learning type voice machine achieved the best results with the lowest WER of speech recognition testing and the highest accuracy in control command for the mobile robot. A generalized speech training database can result in more complete word comprehension, but requires more intensive training methods and more training time. Single-voice training is easier because it is based on a specific set of required commands. This research paper suggests that the performance of the general-purpose speech training database is the best result compared to other research papers. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**WER\%**} & **Accuracy of Speech Recognition** & \multirow{2}{*}{**Fitting Type**} \\  & & & \\ \hline [PERSON] et al. [34] & 7.7\% & X & Universal word type \\ [PERSON] et al. [35] & 11.62\% & X & Universal word type \\ Google API [35] & 15.79\% & X & Universal word type \\ IBM API [35] & 40.74\% & X & Universal word type \\ [PERSON] et al. [36] & 5.8\% & 83.3\% & Single word type \\ [PERSON] et al. [37] & X & 82\% & Single word type \\ [PERSON] et al. [38] & X & 88.5\% & Single word type \\ The proposed & 5.3\% & 89.3\% & Universal word type \\ methods & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons for WER and accuracy of speech recognition in different methods.