## 6 Conclusions  
Based on the observed data, it can be asserted that Alexa performs better in recognizing female speech in Spanish, while, conversely, Whisper exhibits better performance for male speech. However, with a mean difference of 2.94 pp for Alexa (favoring females) and 1.39 pp for Whisper (favoring males), we consider these differences not significant enough to conclude the existence of gender bias in Alexa and Whisper for the Spanish language or at least not a bias that significantly influences the everyday functionality of these tools. This small difference could be attributed to sample error, especially considering the WER for both Alexa and Whisper. More significant differences are observed regarding accents, reaching up to 8 pp in some cases. As a general rule for Alexa, there appears to be a bias in favor of the Northern Spanish accent and against the Southern Spanish accent primarily, as well as other accents such as Caribbean, Central American, and Canarian. For Whisper (Base), there seems to be a potential bias in favor of the northern accent compared to the southern accent in Spain. In the case of Whisper (Largev2), this bias is mainly in favor of the Canarian accent as opposed to the Caribbean accent. An interesting fact regarding the data obtained with Whisper (Large-v2) is that the Canarian is precisely the most accurately recognized despite being the dialectal variant with the fewest speakers. The Canary Islands have just under 2.2 million inhabitants [42], compared to the nearly 500 million people with Spanish as their native language [43]. Concerning the three Alexa models tailored for Mexican Spanish, Spanish from Spain, and Spanish from the United States, there appears to be no compelling reason to maintain these three models as separate entities for the Spanish language. It would be preferable to the optimized model, similar to the approach taken by Whisper. Subsequently, if the goal is to enable Alexa to speak in different dialects, employing a TTS system trained specifically for each accent in question would be more suitable. One of the fundamental limitations of this project arises from the fact that the audio files included in the Common Voice dataset are derived from segments of read texts, in contrast to the natural way of interacting with a voice-activated virtual assistant, which typically involves conversations. Another limitation is that the number of available audio fragments is not comparable between those read by males and females, and a similar issue exists with the distribution across different accents. Concerning future research directions, an obvious avenue would be to extend the study to other languages and their respective dialects, enabling a more comprehensive comparison. Another potential research extension could involve a specific comparison for Alexa, examining the speech-recognition error rate when using a set of predefined words versus a set of non-defined words, as addressed in this study. In many cases, Alexa Skills employs predefined sets of words to facilitate the interaction flow. Analyzing automatic speech recognition with other datasets and for the various languages available for each system would enable us to determine whether progress is genuinely being made in eliminating gender bias in ASR systems or if, in this case, these results are specific to the Spanish language and these two particular tools. Conceptualization, methodology, formal analysis, data curation, E.N.-G.; writing--original draft preparation, E.N.-G.; writing--review and editing, H.S.D.-K.-N. and C.S.G.-G.; supervision C.S.G.-G. All authors have read and agreed to the published version of the manuscript. This work was co-financed by the Canary Islands Agency for Research, Innovation, and Information Society of the Ministry of Economy, Knowledge and Employment and by the European Social Fund (ESF) Integrated Operational Program of the Canary Islands 2014-2020, Axis 3 Priority Topic 74 (85%). This work has been supported partially by the PERGAMEX ACTIVE project, Ref.