## 3 Materials and Methods
### Procedure  
The data analysis procedure is straightforward, primarily involving processing an audio snippet from Common Voice using either Alexa or Whisper. Subsequently, the transcription of the audio is obtained, and this text is then compared with the actual transcription provided by Common Voice. This comparison is achieved by calculating the WER. Finally, after collecting the data, statistical calculations are performed to observe the data jointly based on model variants, gender, or accent variations.  
An important point to consider is that Alexa has three Spanish variants: Mexican, US, and Spain Spanish. This requires conducting all analyses in triplicate to ensure a comprehensive evaluation. In the case of Whisper, there is only a generic model for Spanish, although there are different models of various sizes.  
A Python script with Selenium [37] was created to analyze Alexa, enabling access to the Amazon Alexa developer console and interacting with the system from there. An external USB sound card was used for hardware to eliminate additional noise during sound capture. A simple Alexa Skill (application) was developed, which received an audio input and processed it to respond. Using the Alexa developer console allowed for straightforward capturing of the resulting text. It is worth noting that when developing the Alexa Skill, the generic data type AMAZON.SearchQuery [39] was used because an open and broad dataset was employed. In this case, using more specific data types could potentially influence the speech-recognition analysis results. The issue that needed to be addressed was that with AMAZON.SearchQuery data type, a triggering word needed to precede the phrase for [PERSON] to detect it. In this project, Coqui TTS [38] was used to generate an audio segment with the word \"Escucha\" (Listen) in Spanish, which preceded the segments during the tests. Later on, the word \"Escucha\" was removed from the obtained texts to avoid impacting the results. One of the project requirements for Common Voice is that the segments consist of fewer than 15 words [27]. After conducting various tests by preceding different phrases with the word 'Listen,' no interference was observed. The word 'Listen' was correctly detected in all tests.  
For Whisper, the process was more straightforward, as the system is designed to be used via a Python program that takes an audio file for transcription. Currently, there are six models available for Whisper [7]. For the analysis, the Base and Large-v2 models of Whisper were used. The available models encompass a range of options, including Tiny, Base, Small, Medium, Large, and Large-v2.  
For the analysis, 56,344 segments were processed for Alexa, 202,737 for Whisper (Base), and 105,375 for Whisper (Large-v2). The difference in sample sizes is due to the time constraints of conducting the tests. The Alexa tests took approximately four weeks to complete, while the Whisper Base tests were finished in only four days, and the Whisper Large-v2 teststook approximately one week. It is worth noting that according to data provided by Whisper's creators, the Base model is 16 times faster than the Large or Large-v2 model [7].  
The samples used are listed in Tables 2 and 3 for females and males, respectively. As shown in the tables, a sample of up to a maximum of 2150 audio segments was used in the case of Alexa. If the number is lower, it is because there were no more segments with that accent for that gender that met the established requirements. For Whisper (Base), the samples consisted of all audio segments that satisfied the specified requirements. In the case of Whisper (Large-v2), the limit was set at 11,600 audio segments. In the cases of Alexa and Whisper Large-v2, the selected segments up to the cutoff limit were chosen randomly.