## 3 Materials and Methods
### Inclusion and Exclusion Criteria  
The analysis was conducted on many audio segments sourced from the Mozilla Common Voice dataset for the Spanish language, version 14, released on 28 June 2023 [27]. This dataset comprises 1,608,353 audio fragments and their corresponding text transcriptions, making it suitable for applications such as automatic speech recognition. The dataset encompasses 2175 recorded hours, of which 504 h are validated, involving the participation of 25,261 contributors. In this research, we employed the Common Voice 14 dataset in Spanish to investigate speech recognition in the Spanish language, particularly in identifying potential biases related to gender and accent usage. The Common Voice dataset is subject to human validation, where a segment is considered correct when it receives two positive votes and incorrect when it garners two negative votes. There is also a possibility that some segments may receive both positive and negative evaluations simultaneously. During the data refinement process for the Common Voices dataset, we searched to select segments meeting the following criteria:  
* Must have at least two positive votes and no negative votes to exclude uncertain segments. * Segments are categorized by gender: female and male; this categorization is optional, and not all segments are labeled by gender. * Segments are categorized based on the speaker's accent, as this is also an optional feature when contributing to the Mozilla Common Voice project. Once we obtained the dataset consisting of segments categorized by gender and accent and validated it without negative votes, we analyzed the various accents available. In this case, it should be noted that not all segments use a standardized category for accent classification. This is likely due to the initial stage of the project when the accent field may have been an open-text field rather than a selection field as it currently is. Another filter applied was to ensure that the audio segments consisted of more than one word, as there are segments with single words such as \"Yes\", \"No\", or numbers, which could significantly influence the final analysis results as an error in a single word translates to an entire sentence error. To obtain a sample with a manageable dataset, we proceeded to filter and retain categories where there were at least 500 audio segments for each accent and gender. Following this final filtering, the following categories based on accent were retained:* Central American
* Andean-Pacific: Colombia, Peru, Ecuador, Western Bolivia, Andean Venezuela
* Caribbean: Cuba, Venezuela, Puerto Rico, Dominican Republic, Panama, Caribbean Colombia, Caribbean Mexico, Gulf Coast of Mexico
* Chilean: Chile, Cuyo
* Northern Iberian Peninsula (Astruias, Castilla y Leon, Cantabria, Basque Country, Aragon, La Rioja, Guadalajara, Cuenca)
* Central-Southern Iberian Peninsula (Madrid, Toledo, Castilla-La Mancha)
* Southern Iberian Peninsula (Andalusia, Extremadura, Murcia)
* Canary Islands
* Mexico
* Rioplatense: Argentina, Uruguay, Eastern Bolivia, Paraguay  
After filtering, the total dataset consisted of 202,737 audio segments, which were distributed as follows, as shown in Table 1. Many segments categorized with non-standardized labels were left out, and the only current category available in the Common Voice selection is \"Espanol de Filippinas\". In the dataset, there are only 23 segments for this accent, with 10 from males and 13 from females, and after filtering, it would be reduced to 10 and 5, respectively. The final accent-based categories we have retained closely align with the previously referenced dialect classification [26], with the caveat that, in this context, some accents are further subdivided. The updated classification is as follows: The \"Andean\" accent corresponds to \"Andean\", while the \"Canary\" accent is linked to the \"Canary Islands\". The \"Caribbean\" accent aligns with \"Caribbean\", and the \"Chilean\" accent is associated with \"Chilean\".