## 5 Conclusions  
This paper presents novel solutions to improve speech recognition performance in multi-speaker speech environments. In such settings, where multiple speeches occur simultaneously, traditional speech recognition systems are hard to distinguish between different speakers. In scenarios with overlapping speeches, we introduced the speaker-attributed training automatic speech recognition (SAT-ASR) model, featuring a novel structure and incorporating additional inputs such as speaker embeddings and a speaker profile method. We proposed using \(i\)-, \(d\)-, and \(x\)-vectors as effective cues to split the multiple-speaker information mixed in the input features. We also proposed utilizing a profile matrix constructed by the embedding vectors of the enrolled speakers. Because speaker embedding vectors are extracted from mixed speech, they are regularized by being replaced with the linearly weighted combinations of the enrolled embedding vectors. The effectiveness of the proposed method was verified by multi-speaker speech recognition experiments on _LibriMix_ datasets. A comparative analysis of using the speech separation implemented by DPRNN-TasNet as inputs to the single-speaker ASR model, conventional multi-speaker ASR models implemented by Transformer networks, and the proposed SAT-ASR models were carried out. The word error rates (WERs) measured on a designated test set of _LibriMix_ were 13.6%, 8.43%, and 8.1%, respectively. When employing speaker profiles, the SAT-ASR model further improved its WER to 7.48%, surpassing all the other multi-speaker ASR solutions. The computational efficiencies were also compared in terms of the number of trainable parameters and training times. The proposed methods required 336 and 328 GPU hours with and without speaker profiles, while the baseline multi-speaker model required 320 GPU hours, resulting in up to 5% additional training time. One of the major advantages of the proposed methods was that the pretrained components of the single-speaker ASR models can be reused without fine tuning by multi-speaker utterances. Those parts were speech recognition encoders yielding text outputs. Training a single-speaker ASR model from scratch required 892 h, but it was not required to be updated again. These methods not only improved the accuracy of the speech recognition systems, but they were also adaptable to a variety of complex speech scenarios with a small amount of computational overhead. Future research will focus on expanding the SAT-ASR model to be applicable to scenarios with an arbitrary number of speakers. To achieve this, we will explore techniques for automatically detecting the number of speakers in an audio input and adjusting the encoder of the proposed model accordingly. Additionally, we aim to improve the model's structure to ensure that, when recognizing a single-speaker speech in multi-speaker environments, the computational load is comparable to that of conventional single-speaker ASR models. \begin{table}
\begin{tabular}{|p{142.3 pt}|p{142.3 pt}|} \hline  & **Token Level Text** \\ \hline Label-A & SHE WAS YOUN TRI ED NERVOUS IT WA A VISION OF SERIOUS DUTIES AND LITTLE COMPANY OF REALLY GREAT LO NE LINESS \\ \hline Result-1 & SHE WAS YOUN UN TRI ED NERVOUS IT WA A VISION OF SERIOUS DUTIES AND LITTLE COMPANY \_BUT REALLY GREAT LO NE LINESS \\  & \textless{}eos\textgreater{} \\ \hline Label-B & HE HOPED THERE WOULD BE STE W FOR DINNER TURN IP S AND CARR OT S AND B RU IS ED POT A TO ES AND FAT MU T TON PIECES TO BE LAD LED OUT IN THICK PEPPER ED FLOUR FAT TEN ED SAUCE \\ \hline Result-2 & HE HOPED THERE WOULD BE STE W FOR DINNER TURN IP S AND CARR OT S AND B RU IS ED POT A TO ES AND FAT MU T TON PIECES TO BE LAD LED OUT IN THICK PEPPER ED FLOUR FAT AND SAUCE \\  & \textless{}eos\textgreater{} \\ \hline \end{tabular}
\end{table} 
Table 3: An example of the output word sequences generated by the proposed method.