## 4 Experimental Performance Evaluation
### Evaluation Results and Discussions  
To determine the best-performing speaker embedding and profile settings for the proposed SAT-ASR model, we evaluated the performance of various profile and speaker embedding configurations. The first set of experiments confirmed performance changes depending on the number of utterances used per profile. Figure 5 shows the word error rate (WER) variations according to the number of profile utterances. In this experiment, the SAT-ASR model using \(x\)-vector speaker profiles was used for evaluation. The \(x\)-axis is the number of utterances per profile. When the number of utterances (single-speaker samples) for each profile was 1, the lowest performance was 8.61% WER. When the number of utterances per profile was set to 5, 10, and 20, the performance improved to 7.83%, 7.57%, and 7.48%, respectively. Performance increased as the number of utterances for each profile increased, but it remained the same after 20. Based on these results, 20 utterances per profile were used in the speech recognition performance evaluation. Table 2 compares the proposed SAT-ASR models with conventional models. The number of trainable parameters, training time, and test WER are listed. For the single-speaker automatic speech recognition (SS-ASR) model, we adopted a simple Transformer-based architecture [44] because the proposed SAT-ASR methods require pretrained decoders from the SS-ASR. Its implementation recipe was retrieved from _ESPnet_ repository at [[https://github.com/espnet/espnet/tree/master/egs2/librispeech/asr1](https://github.com/espnet/espnet/tree/master/egs2/librispeech/asr1)]([https://github.com/espnet/espnet/tree/master/egs2/librispeech/asr1](https://github.com/espnet/espnet/tree/master/egs2/librispeech/asr1)), accessed on 20 July 2024, where WER was reported for the single-speaker input and the _test-clean_ set of _LibriSpeech_ was 2.5. The SS-ASR model has 113 million (denoted by 'M' in Table 2) trainable parameters, and the total training data consists of 460 h of the _train-clean_ subset of _LibriSpeech_[36]. It took 892 GPU hours to train all the parameters from scratch. As shown in Table 1, the test data were 3000 utterances of the _test-clean_ subset of _Libri2 Mix_ generated by _LibriMix_. The evaluated WER was 76.9%, which is significantly worse than the _LibriSpeech_ test dataset. The huge degradation was due to the mismatch between the single-speaker ASR model and multi-speaker input recordings. The second model uses a DPRNN-TasNet (dual-path recurrent neural network-based time-domain audio separation network) [45] to separate the speakers in the mixed-speech input. After separation, the word sequence of each output was generated using the SS-ASR, and the WER of each channel was measured. Because the pretrained SS-ASR was used, only a DPRNN-TasNet was required to be trained, so the number of trainable parameters was only 2.6 million. It took 212 GPU hours to train the DPRNN-TasNet model measured by the same NVIDIA GTX Titan GPU. The WER on the _test-clean_ set of _Libri2 Mix_ was 13.6%, which a significant improvement on SS-ASR. \begin{table}
\begin{tabular}{|l|l|l|l|} \hline
**Model** & \begin{tabular}{l} **Trainable** \\ **Parameters** \\ \end{tabular} & \
\begin{tabular}{l} **Training Time** \\ **(GPU Hours)** \\ \end{tabular} &
\begin{tabular}{l} **Test** \\ **WER (\%)** \\ \end{tabular} \\ \hline SS-ASR [44] & 113 M & 892 & 76.90 \\ \hline DPRNN-TasNet + SS-ASR [45] & 2.6 M & 212 & 13.60 \\ MS-ASR [6] & 60 M & 320 & 8.43 (-) \\ \hline MS-ASR + SAT & \(i\)-vector & 60 M & 320 & 8.50 (\(-\)0.8) \\ (proposed) & \(d\)-vector & 71.2 M & 336 & 8.32 (1.3) \\  & \(x\)-vector & 64.2 M & 328 & 8.10 (3.9) \\ \hline MS-ASR + SAT & \(i\)-vector & 60 M & 320 & 8.27 (1.9) \\ + speaker profile & \(d\)-vector & 71.2 M & 336 & 8.18 (3.0) \\ (proposed) & \(x\)-vector & 64.2 M & 328 & 7.48 (11.2) \\ \hline \end{tabular}
\end{table}