# Automatic Speech Recognition Advancements for Indigenous Languages of the Americas
## 4 Results  
In this section, we delve into the outcomes of our investigation, where we explore the intricate interplay between hyperparameter configurations and the performance of diverse language models. The ensuing analysis, encapsulated in Table 3, summarizes the best hyperparameter configuration obtained for each language. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Minimum Value** & **Maximum Value** \\ \hline Learning rate & \(10^{-6}\) & \(10^{-3}\) \\ Max number of updates & 10k & 100k \\ Freeze fine-tune updates & 0 & 50k \\ Activation dropout & 0.01 & 0.2 \\ Mask probability & 0.2 & 0.7 \\ Mask channel probability & 0.1 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter search range during the fine-tuning training phase. The Quechua language model, benefiting from a substantial training dataset of 12.09 h, achieved the best performance, with a WER of 48.98% and a CER of 12.14%. This performance could be influenced not only by the amount of data but also by the quality and diversity of the data. However, our investigation also unveiled a counter-intuitive observation: the Kotiria language model, with the most extensive dataset of 29.92 h (see Table 1), showed the highest WER (79.69%) and CER (36.59%) rates. This discrepancy could arise from language complexity, data quality, or diversity. Remarkably, the Guarani language model, trained on the smallest dataset (0.97 h), achieved a WER of 62.91% and a CER of 15.59%. The notable effectiveness of meticulous hyperparameter tuning, even with limited data, highlights the importance of this process. The precise adaptation of hyperparameters can somewhat compensate for data scarcity, optimizing model performance. This trend extended to the Bribri and Wa'ikhana language models, which exhibited intermediate performance levels despite varying dataset sizes (2.38 and 6.11 h, respectively). These outcomes, as shown in Table 3, underscore the pivotal role of hyperparameter tuning in determining model performance. Additional configurations and results can be found in the Supplementary Information. In addition, our research examined two variants of the Wav2 vec2.0 XLS-R model: one with 300 million parameters and another with 1 billion parameters. Despite the difference in parameter count, both models yielded comparable results, except for Kotiria, where the bigger model showed significantly better performance. This suggests that the choice between these two models may be influenced by resource constraints, where the smaller model may be preferable for applications with limited resources, while the larger model could offer slightly improved performance when more data are available. Analyzing the hyperparameters across different languages, it is noticeable that the learning rate is consistently set at either \(1\times 10^{-5}\) or \(1\times 10^{-4}\), although the search range is wider (see Table 2). This low learning rate aligns with previously reported investigations [26; 29; 61], allowing precise weight adjustments during the fine-tuning phase. The learning rate is highly related to the maximum number of updates. In this case, the total number of iterations the model trains varies widely, from 40k to 130k. However, no apparent simple relationship exists between the number of updates, the optimization step size, or the available training datasets illustrated in Figure 2. This suggests the requirement of different amounts of training depending upon the complexity and uniqueness of the languages. The fine-tuning freezing point, denoting the end at which specific model layers are frozen and not updated, seems to increase with the maximum number of updates for the Quechua, Bribri, and Guarani languages. However, this pattern does not hold for the Wa'ikhana language, which combines a lower freezing point with more updates. The mask channel values hover between 0.25 and 0.5, implying the significance of masking specific inputs during training, potentially reducing overfitting.