# Speech Recognition for Task Domains with Sparse Matched Training Data
## 5 Experimental Results
### Experiments on Teacher/Student-Based Transfer Learning Using Augmented Training Data  
In this experiment, we evaluated the performance of transfer learning based on teacher/student learning and the use of augmented training data. First, we implemented the designed integrated system consisting of VAEs, which generate latent variables with disentangled attributes, and the following module, which generates the augmented target domain data using the paired target domain and general domain speech data as input data, as shown in Figure 4.  
We evaluated the proposed data augmentation method (described in Figure 4) for the ASR task with the AMI meeting corpus. Table 5 shows the speech recognition performance of the proposed method. We trained three models using different training DB: IHM-only, SDM-only, and an augmented DB using the proposed method to add to SDM. For each IHM-only and SDM-only DB, we used the standard train/dev/eval data partitions of the AMI meeting corpus. The AugSDM was generated using the proposed method by maintaining the phonetic contents of IHM and substituting channel attributes from the random sample of SDM. As shown in Table 5, the model trained using only IHM suffers from a sharp degradation in performance due to the large mismatch in channel conditions between IHM and SDM. The matched SDM training and evaluation condition shows improved results, and even further performance improvement was obtained by adding the AugSDM generated by the proposed method. Similar to the experimental results presented in Table 5, [PERSON] et al. [45] proposed a teacher-student learning approach for unsupervised domain adaptation and reported the results for domain adaptation from AMI-IHM speech to AMI-SDM speech. They used an architecture with time-delayed neural network layers interleaved with LSTM for their experiment. As we used the \begin{table}
\begin{tabular}{c c} \hline \hline
**Training DB** & **Error Rate** \\ \hline
500 h Korean call center recording & 21.1 \\ +1000 h randomly selected Korean broadcast speech & 19.4 \\ +1000 h actively selected Korean broadcast speech & 18.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of syllable error rate (%) on the call center recording speech recognition task, comparing the models trained using the speech DB selected by the proposed active learning method and the random selection method. Figure 7: Spatial distribution of sequence-level attributes in the acoustic space of call center recordings, broadcast speech, unselected DB, and selected DB. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training DB** & **Eval** & **Dev** \\ \hline IHM & 55.9 & 51.1 \\ SDM & 35.0 & 32.3 \\ SDM + AugSDM & 32.5 & 29.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of character error rate (%) on the AMI meeting corpus task, for models trained using individual headset microphone (IHM) only, single distant microphone (SDM) only, and SDM + AugSDM for the evaluation and development set of SDM. end-to-end ASR system architecture for our experiment, directly comparing the performance of our study with that of the previous study is difficult. However, when the relative degrees of improvement in performance over the baseline model are compared, our proposed method provides better results than that used in the previous study. We then applied the proposed data augmentation method described in Figure 4 to the call center recording speech recognition task, a task domain with sparse matched training data. Figure 8 shows the spatial distribution of sequence-level attributes in the acoustic space for call center recordings, broadcast speech, and the augmented DB, which are visualized through t-SNE. Figure 8 shows that the call center recording and broadcast speech DBs have somewhat distinct spatial distributions. This is due to the difference in the channel/noise levels and other environmental factors.