# Automatic Speech Recognition Advancements for Indigenous Languages of the Americas
## 4 Results
Finally, activation dropout rates are consistently low across the models, between 0.1 and 0.2, suggesting a regularization strategy to prevent overfitting while maintaining the capability to learn complex patterns. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Language** & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & **Max Updates** & \begin{tabular}{c} **Freeze** \\ **Fine-Tune** \\ \end{tabular} & \
\begin{tabular}{c} **Mask** \\ **Channel** \\ \end{tabular} &
\begin{tabular}{c} **Activation** \\ **Dropout** \\ \end{tabular} & **WER** & **CER** \\ \hline Quechua & \(1\times 10^{-5}\) & 90k & 5k & 0.5 & 0.1 & 48.98 & 12.14 \\ Kotiria & \(1\times 10^{-5}\) & 40k & 5k & 0.5 & 0.1 & 79.69 & 36.59 \\ Bribri & \(1\times 10^{-4}\) & 90k & 8k & 0.3 & 0.2 & 69.03 & 34.70 \\ Guarani & \(1\times 10^{-5}\) & 90k & 9k & 0.3 & 0.1 & 62.91 & 15.59 \\ Wa’ikhana & \(1\times 10^{-5}\) & 130k & 1k & 0.25 & 0.1 & 68.42 & 35.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: WERs and CERs for the best fine-tuning hyperparameter configurations for Quechua, Kotiria, Bribri, Guarani, and Wa’ikhana. Additional configurations and results can be found in the Supplementary Information. Specific hyperparameters did not correlate directly with lower WER or loss values. This suggests that the optimal hyperparameters are highly dependent on language-specific characteristics. Nonetheless, it is worth noting that, despite similar learning rates, mask channel probabilities, and activation dropout rates, the Guarani language model achieved notably lower WER and loss values, hinting at the influence of factors like dataset quality, model architecture, or inherent language characteristics. The Sobol' sensitivity analysis reveals that the number of fine-tuning updates and the activation dropout rate are critical for model performance. This indicates that careful attention to these hyperparameters can significantly improve results. Interestingly, the low impact of the maximum number of updates suggests that other factors, such as layer freezing and regularization, are more influential in data-limited contexts. Table 4 presents the outcomes of the Sobol' sensitivity analysis, offering insights into the relative significance of each hyperparameter in influencing the WER of the language model. This analysis delves into the influence of six key hyperparameters: learning rate, max number of updates, freeze fine-tune updates, mask prob, mask channel prob, and activation dropout, on the model's WER. The first-order sensitivity (S1) index measures the individual contribution of the parameter to the total output variance, while the total sensitivity (ST) index reflects the total contribution of a parameter, including its interactions with the other hyperparameters, to the final output. Interestingly, hyperparameters with the most substantial individual influence on the WER (S1), e.g., learning rate (ST = 0.57) or masked channel probability (S1 = 0.54), do not have so high an impact when considering their interactions with other hyperparameters (ST = 0.26 and ST = 0.28, respectively). Notably, the hyperparameter with a higher ST is the freeze fine-tuning updates, suggesting that a bad choice could lead to _forgetting_ the info in the pre-trained layers or not _adapting_ the pre-trained neural network to the current domain. On the other hand, unexpectedly, the maximum number of updates during training seems not so sensible, although it is usually a parameter to which the research community shows more attention. Finally, activation dropout is the second most important contribution to the output variance on the ST, which is somehow expected, as it is a sensible tuning that avoids overfitting but which, in turn, can make training difficult when not properly adjusted. Figure 2: The outer bar chart panel displays the character error rates (CERs) for five Indigenous language models: Kotiria, Waikhana, Bribri, Guarani, and Quechua. Lower bars indicate better-quality performance of the model.