Alexa, Do What I Want To. Implementing a Voice Spoofing Attack Tool for Virtual Voice Assistants

Nacimiento-García, Eduardo; Caballero-Gil, Cándido; Nacimiento-García, Andrés; González-González, Carina S.

doi:10.1007/978-3-031-21333-5_41

Cited by 1 publication

(1 citation statement)

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“…Other error measures such as Character Error Rate (CER), Match Error Rate (MER), Word Information Lost (WIL), and Word Information Preserved (WIP) are also included in the datasets. The data are divided based on the tool used, i.e., Alexa, Whisper Base, or Whisper Large-v2, and within these categories, the data is further divided by gender and accent [41]. Lost (WIL), and Word Information Preserved (WIP) are also included in the datasets.…”

Section: Resultsmentioning

confidence: 99%

Gender and Accent Biases in AI-Based Tools for Spanish: A Comparative Study between Alexa and Whisper

Nacimiento-García,

Díaz-Kaas-Nielsen,

González-González

2024

Applied Sciences

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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.

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Section: Resultsmentioning

confidence: 99%