## 5 Discussion
Figure 6: Comparative of weighted means WER by gender. Let us analyze the weighted means shown in Table 17 and Figure 6. We can observe that in all cases, for the same type of Alexa variant, female voices are slightly better recognized than male voices, meaning the mean WER is lower for women than for men. On the other hand, for Whisper, the opposite is true--the mean WER is slightly lower for men than for women. More specifically, we see that in Alexa, the difference between the mean WER for female and male voices is 3.24 pp (percentage points), 2.69 pp, and 2.88 pp, respectively, for Alexa MX, Alexa ES, and Alexa US, in favor of female voices. In the case of Whisper, these differences are 1.19 pp and 1.58 pp for the Base and Large-v2 models, respectively, but in favor of male voices. Table 18 and Figure 7 show the average data for each of the Alexa and Whisper variants, regardless of gender. In these weighted mean data for each accent, each variant of Alexa and Whisper was analyzed, and it can be observed that the Alexa US model performs the worst in all cases compared to the other variants. In all other cases, the Alexa ES variant is slightly better than the Alexa MX variant, except for the Canarian accent, where it is the opposite. It is noticeable that in some instances, the difference in the weighted means WER by accent; for example, for Alexa, it can reach up to 6.76 pp. This is the case when comparing the Spanish Northern and Spanish Southern accents. For Alexa MX, the difference between the Northern and Southern Spanish accents can be as high as 7.64 pp. For Alexa US, 7.98 pp between the same accents. These differences are observed across the various accents as well. In Whisper (Base), approximately 8 pp differences are observed between the Northern Spanish accent and the Caribbean, Mexican, and Southern Spanish accents. In Whisper (Large-v2), a difference of around 4 pp between the Canarian and Caribbean accents is noted. When analyzing the confidence intervals (CI 95%) calculated for each of the available options, it is observed that, overall, the sample fits appropriately and is accurately repre \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Gender**} & **Alexa** & \multirow{2}{*}{**Alexa ES**} & **[PERSON]** & **Whisper** & **Whisper** \\  & **MX** & & **US** & **Base** & **Large-v2** \\ \hline Andean & 41.66 & 38.44 & 46.05 & 23.16 & 7.68 \\ Canary & 39.76 & 41.29 & 49.49 & 20.76 & 6.18 \\ Caribbean & 41.91 & 39.54 & 47.98 & 26.38 & 10.06 \\ Central American & 42.79 & 41.01 & 47.72 & 21.16 & 8.31 \\ Chilean & 41.08 & 40.13 & 47.74 & 21.95 & 7.43 \\ Spain Central & 38.99 & 36.58 & 43.96 & 20.98 & 8.47 \\ Spain Northern & 37.85 & 34.81 & 42.59 & 18.34 & 7.82 \\ Spain Southern & 45.49 & 41.57 & 50.57 & 26.86 & 9.05 \\ Mexican & 41.50 & 36.26 & 45.97 & 26.06 & 8.78 \\ Rioplatense & 40.26 & 37.32 & 44.07 & 20.86 & 6.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Weighted means WER by accent. Figure 7: Comparative of weighted means WER by Accent. sented by these statistical measures. A wider confidence interval is notable in the case of Whisper Large-v2 for females, especially for Caribbean, Central American, and Central Spanish accents, although it is also significantly high for the Northern Spanish accent, albeit to a lesser extent. Although these values encompass a broader probable range than desired, they are not sufficiently large to invalidate this portion of the results. In the remaining cases, for both genders and all analyzed tools, the confidence intervals are below 2, and even in many cases below 1, with a slight exception for Canary Islands females and Alexa variants, where we obtain 2.88 for MX, 3.13 for ES, and 3.49 for US. However, considering that the mean WER values for these variants are 35.46, 42.52, and 51.19, respectively, it is concluded that these values are still significant. In previous studies, the analysis of gender and accent bias within the same language has been explored.