# Speech Recognition for Task Domains with Sparse Matched Training Data
## 5 Experimental Results  
### Experiments for Active Learning Using Latent Variables with Disentangled Attributes  
In this experiment, we evaluated the performance of the proposed active learning method using latent variables with disentangled attributes. We implemented the designed integrated system, consisting of an encoder that infers the latent variables and a DNN-based classifier that selects training data with attributes matching the target domain, using the preceding encoder output as input. First, for the functional verification of the integrated systems, we measured the performance of the classifier using the AMI meeting corpus. The factorized hierarchical VAEs were trained using 265k utterances from the SDM and IHM channels of the AMI meeting corpus. The classifier was trained using 10k utterances from the same channels. The classification performance of300 utterances from the SDM and IHM corpus was evaluated and the results are shown in Table 2. In general, when the utterances are longer, the classification performance is better. However, as an input vector, the sequence-level latent variable outperforms mel-frequency cepstral coefficients (MFCC) [43] and log-mel filter banks [44]. Indeed, its classification results remain robust even for short utterances. We then evaluated the proposed active learning method for the non-native Korean speech recognition task, which is a task domain with sparse matched training data. The proposed integrated system consisted of VAEs with an encoder that infers latent variables with disentangled attributes and a classifier that actively selects training data with attributes matching the target domain. The system was trained using 520 h of both non-native Korean speech corpus and Korean broadcast speech corpus. Figure 6 shows the result obtained by visualizing the spatial distribution of sequence-level latent variables in the acoustic space through t-distributed stochastic neighbor embedding (t-SNE), which is the encoder output of trained VAEs with non-native Korean speech and native Korean speech as input. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Dimension** & \begin{tabular}{c} **Above** \\ **1 s** \\ \end{tabular} & \begin{tabular}{c} **Above** \\ **2 s** \\ \end{tabular} & \
\begin{tabular}{c} **Above** \\ **5 s** \\ \end{tabular} &
\begin{tabular}{c} **Above** \\ **10 s** \\ \end{tabular} \\ \hline MFCC & 40 & 4.7 & 4.0 & 2.0 & 1.7 \\ LogMel & 40 & 2.7 & 3.0 & 2.7 & 2.2 \\ Sequence Level Latent variable(Z\({}_{2}\)) & 32 & 1.0 & 0.3 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification error rate for different utterance times in seconds. Table 3 shows the speech recognition performance of the models trained by the speech DB selected by the proposed active learning method. All models in Table 3 involve an end-to-end ASR system, whose detailed architecture was described in Section 4.2. We trained the baseline model using 200 h of non-native Korean speech DB, and then compared two versions of the model. One model was trained using 500 h of additional native Korean speech DB selected by the proposed active learning method, and one was trained by equal amounts of speech DB selected randomly. The evaluation set was comprised of 515 utterances recorded for assessing a Korean tutoring service for foreigners. As shown, there is a significant improvement with the proposed active learning method. We applied the proposed active learning method to the call center recording speech recognition task, which is another task domain with sparse matched training data. Figure 7 shows the result obtained by visualizing the spatial distribution of call center recordings, broadcast speech, selected DB, and unselected DB through t-SNE. In Figure 7, the selected and unselected DBs are both sampled from broadcast speech. Among them, the selected DB was selected by the proposed active learning method, while the unselected DB is the remaining part of the DB that was not selected.