## 5 Discussion  
After analyzing the results, it is observed that, as a general rule, the Alexa variant for U.S. Spanish, here identified as Alexa US, performs the worst among the three Alexa variants for any of the analyzed accents. In the case of the other two Alexa variants, for Mexican Spanish and Spanish from Spain, identified here as Alexa MX and Alexa ES, respectively, the comparison of means reveals a similar performance, with a slight improvement in Alexa ES compared to Alexa MX, except for the case of females from the Canary Islands, where Alexa MX performs better. In the case of median comparisons, we see virtually the same result, except for several cases with the same median for both Alexa MX and Alexa ES. Additionally, there are two cases where Alexa MX outperforms Alexa ES, and these cases are for both genders of the Canary accent, although the difference is more pronounced for females. When comparing the results obtained in the different Alexa variants with the results of Whisper (Base), it is generally observed that Whisper performs significantly better than Alexa for any of the analyzed accents, except for the variant spoken in the Southern Iberian Peninsula and for males. Median values for Alexa are around 30%, while Whisper has about 15% for WER. Generally, Whisper (Base) makes approximately half the WER errors as Alexa with the Spanish language. In the case of Whisper (Large-v2), it is observed that the results significantly improve compared to Whisper (Base), with a mean below 10% for both women and men. Particularly indicative are the median values of Whisper (Large-v2) for both genders, as they achieve medians of 0% for all cases except for the Spanish of the Southern Iberian Peninsula. Figure 4: Comparative of males’ medians WER. Figure 5: Comparative of weighted means. These data reveal a clearly identifiable outlier for Whisper, both for the Base model and the Large-v2 model, in the accent of Spanish from the southern part of the Iberian Peninsula when spoken by males. After analyzing the Common Voice 14 dataset, it is observed that thousands of contributions could be from the same person, which may have influenced the final result of the analysis for this accent. Taking into account that the selected sample for the male gender and the accent of the Southern Iberian Peninsula was 30,698 audio segments for Whisper (Base), 11,600 for Whisper (Large-v2), and 2150 for Alexa, it is possible that this influence did not affect the analysis of Alexa as much as it did for Whisper. If the analysis results for females had also deteriorated for this accent, it would not have been assumed that something was affecting the male results. In the case of using the same random sample for Whisper (Base) for the Southern Iberian Peninsula accent as used for Alexa, the results obtained are shown in Table 15. Clearly, there is bias in the dataset affecting the results. The median values for Whisper (Base) are 16.67%, which is closer to the results obtained for females for this accent. For Whisper (Large-v2), Similar results were obtained, with a median of 0.00%, which is quite similar to the results obtained for females. These data can be seen in Table 16.  
If the weighted means obtained previously and shown in Table 14 and Figure 5 are recalculated using this correction for this accent, the data obtained is shown in Table 17 and Figure 6. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Gender** & **Mean** & **Median** & **Stdev** & **Variance** & **CI 95\%** \\ \hline Female & 25.52 & 16.67 & 33.79 & 11.42 & 1.74 \\ Male (2150) & 26.92 & 16.67 & 49.60 & 24.60 & 2.10 \\ \hline \end{tabular}
\end{table} 
Table 16: Whisper (LargeV2): South of Spain. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Gender** & **Alexa** & **Alexa ES** & **Alexa** & **Whisper** & **Whisper** \\  & **MX** & **US** & **Base** & **Large-v2** \\ \hline Female & 39.30 & 36.92 & 44.72 & 24.76 & 9.42 \\ Male & 42.54 & 39.61 & 47.60 & 23.57 & 7.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Weighted means WER by gender.