## 4 Experiments
### Dataset Base  
This study utilized the LibriSpeech dataset, a widely used large-scale corpus for speech recognition research. It comprises approximately 1000 h of English speech recordings from the public domain, encompassing diverse speech styles, accents, and topics. This dataset provides extensive resources for developing and evaluating speech recognition algorithms, ensuring the generalizability of experimental results.  
In this study, the train-960 subset was used for model training, while dev-clean, dev-other, test-clean, and test-other were employed for validation and testing. The clean subsets include recordings characterized by clear speech, standardized pronunciation, and minimal background noise, primarily aimed at evaluating model performance under ideal conditions. In contrast, the other subsets represent recordings from more complex acoustic environments and are designed to assess the model's robustness. These recordings are marked by distinctive features such as heavy accents, irregular pronunciations, rapid speaking rates, and low-quality audio caused by aging recording equipment or environmental factors. Additionally, they include various background noises, such as indoor reverberation, street sounds, and low-frequency environmental disturbances. Testing on these subsets, particularly dev-other and test-other, enables a comprehensive evaluation of the model's adaptability and robustness across diverse speech scenarios, showcasing its ability to handle noisy and complex speech conditions effectively. Detailed information about the dataset is presented in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Output Size** & **Input Size/Patch Size, Stride** \\ \hline Input & 80-d & 1000 \(\times\) 80 \\ Conv2D (input layer) & 250 \(\times\) 20 \(\times\) 256 & 80 \(\times\) T, 4 \(\times\) 4, stride 2 (time and freq) \\ Positional encoding & 250 \(\times\) 256 & 250 \(\times\) 256 \\ Conformer block \(\times\) 12 & 250 \(\times\) 256 & 250 \(\times\) 256 \\ Feed-forward layer & 250 \(\times\) 256 & 250 \(\times\) 256 \\ Self-attention layer & 250 \(\times\) 256 & 250 \(\times\) 256 \\ CNN module & 250 \(\times\) 256 & 250 \(\times\) 256 \\ Fully connected layer & 5002-d & 250 \(\times\) 256 \\ Output & 5002-d & 250 \(\times\) 5002 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model architecture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Subset** & **Audio Duration(h)** & **Word** & **Sampling Rate** & **Format** \\ \hline \multirow{3}{*}{Training set} & train-clean-100 & 100 & 5,185,647 & \multirow{3}{*}{16 kHz} & \multirow{3}{*}{flac} \\  & train-clean-360 & 360 & 18,668,330 & & \\  & train-clean-500 & 500 & 25,928,235 & & \\ \hline \multirow{3}{*}{Validation set} & dev-clean & 5.4 & 53,167 & \multirow{3}{*}{16 kHz} & \multirow{3}{*}{flac} \\  & dev-other & 5.3 & 52,183 & & \\ \hline \multirow{3}{*}{Test set} & dev-other & 5.4 & 53,958 & \multirow{3}{*}{16 kHz} & \multirow{3}{*}{flac} \\  & test-other & 5.1 & 50,961 & & \\ \hline \multicolumn{2}{c}{Total} & 981.2 & 49,992,481 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset configuration.