###### Abstract  
Considering previous research indicating the presence of biases based on gender and accent in AI-based tools such as virtual assistants or automatic speech recognition (ASR) systems, this paper examines these potential biases in both Alexa and Whisper for the major Spanish accent groups. The Mozilla Common Voice dataset is employed for testing, and after evaluating tens of thousands of audio fragments, descriptive statistics are calculated. After analyzing the data disaggregated by gender and accent, it is observed that, for this dataset, in terms of means and medians, Alexa performs slightly better for female voices than for male voices, while the opposite is true for Whisper. However, these differences in both cases are not considered significant. In the case of accents, a higher Word Error Rate (WER) is observed among certain accents, suggesting bias based on the spoken Spanish accent. accent bias; Alexa; ASR; automatic speech recognition; gender bias; Whisper +
Footnote †: journal: Medical  
French, Russian, Portuguese, and Urdu), we will encompass approximately 66% of the global population, leaving over 2.7 billion people (34%). The top 200 languages spoken account for approximately 88% of the world's population [8]. Access to information and human knowledge by all individuals, regardless of their language, is paramount and should be regarded as a fundamental right. Thanks to the Internet, access to a portion of information and human knowledge has become more democratic, yet much remains to be accomplished [12]. According to UNESCO, roughly 781 million people worldwide are illiterate, with approximately two thirds of them being women [13]. Hence, voice interaction technology must be adept enough to interpret a wide range of languages, dialectal variations, and accents, regardless of gender. Previous studies have revealed historical automatic speech recognition (ASR) system biases [14; 15]. These biases hinder effective communication for certain groups of people when using voice recognition systems [16]. Some of these biases [17; 18] may be attributed to cultural, social, medical, or other differences, making gender and dialectal variations or accents of the interacting individuals two significant sources of potential bias within ASR systems [19; 20]. In this context, it is relevant to consider potential gender biases [21; 22] in speech-recognition tools and virtual assistants, both in the responses provided by an assistant and in the actual voice recognition. This study will specifically focus on speech recognition. In general terms, gender identification through voice primarily relies on fundamental frequency [23]. On average, female voices have a fundamental frequency of approximately one octave higher than male voices [24]. Fundamental frequency refers to the lowest vibration frequency of the vocal cords during sound production. Typically, female vocal cords tend to be shorter and thinner than male vocal cords, resulting in a higher fundamental frequency in female voices and a lower one in male voices [25]. As previously mentioned, in addition to the various languages, different dialects exist within languages. In our case, we will focus on the dialects of the Spanish language. One of the current classifications identifies eight dialectal regions of the Spanish language [26], with five in the Americas, two in Europe, and one in Africa. The dialectal regions encompass the following areas: America, which includes the Caribbean, Mexican-Central American, Andean, Austral, and Chilean regions; Europe, consisting of the Northern Iberian Peninsula (Septentrational) and the Southern Iberian Peninsula (Meridional); and Africa, which comprises the Canary Islands. This research aims to ascertain whether there is any significant bias concerning gender or the main accents of the Spanish language. To achieve this, audio clips from the Common Voice 14 dataset by Mozilla [27] are analyzed using both Alexa and Whisper. Section 3 introduces the tools and datasets employed for the analyses.