Data Augmentation for Speaker Identification under Stress Conditions to Combat Gender-Based Violence

Rituerto-González, Esther; Mínguez-Sánchez, Alba; Gallardo-Antolín, Ascensión; Peláez-Moreno, Carmen

doi:10.3390/app9112298

Cited by 20 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the paper from Khan et al [6] explores the use of Boltzmann Machines to speaker clustering and tracking. Furthermore, the research conducted by Rituerto-González et al [7] apply speaker identification techniques in a real application under adverse conditions (see Section 2.6).…”

Section: Speaker Verification and Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Editorial for Special Issue “IberSPEECH2018: Speech and Language Technologies for Iberian Languages”

2020

View full text Add to dashboard Cite

The main goal of this Special Issue is to present the latest advances in research and novel applications of speech and language technologies based on the works presented at the IberSPEECH edition held in Barcelona in 2018, paying special attention to those focused on Iberian languages. IberSPEECH is the international conference of the Special Interest Group on Iberian Languages (SIG-IL) of the International Speech Communication Association (ISCA) and of the Spanish Thematic Network on Speech Technologies (Red Temática en Tecnologías del Habla, or RTTH for short). Several researchers were invited to extend their contributions presented at IberSPEECH2018 due to their interest and quality. As a result, this Special Issue is composed of 13 papers that cover different topics of investigation related to perception, speech analysis and enhancement, speaker verification and identification, speech production and synthesis, natural language processing, together with several applications and evaluation challenges.

show abstract

Section: Speaker Verification and Identificationmentioning

confidence: 99%

“…The article by Rituerto-González et al [7] deals with the development of a speaker identification robust to emotional bias. The system is designed to run on a personalized wearable pendant, which is being developed within the Bindi project as a smart solution to improve women's safety.…”

Section: Applications Of Speech Technologiesmentioning

confidence: 99%

Editorial for Special Issue “IberSPEECH2018: Speech and Language Technologies for Iberian Languages”

2020

View full text Add to dashboard Cite

show abstract

“…Prac-tically speaking, it would be relevant to investigate the extent to which data acquisition methods related to emotion recognition and sexual harassment might apply. Among potential indicators one could think of, for example, the human's lack of allowing turn-taking in communication, tone of voice and body posture, neglect of robotic non-verbal signals of noninterest, and so on (see, e.g., [36,38]). As a second step, investigations regarding the applicability of machine learning techniques are relevant (e.g., [23]).…”

Section: Implications Of Virtuous Sex Robotsmentioning

confidence: 99%

Designing Virtuous Sex Robots

Peeters

Haselager

2019

Int J of Soc Robotics

View full text Add to dashboard Cite

We propose that virtue ethics can be used to address ethical issues central to discussions about sex robots. In particular, we argue virtue ethics is well equipped to focus on the implications of sex robots for human moral character. Our evaluation develops in four steps. First, we present virtue ethics as a suitable framework for the evaluation of human-robot relationships. Second, we show the advantages of our virtue ethical account of sex robots by comparing it to current instrumentalist approaches, showing how the former better captures the reciprocal interaction between robots and their users. Third, we examine how a virtue ethical analysis of intimate human-robot relationships could inspire the design of robots that support the cultivation of virtues. We suggest that a sex robot which is equipped with a consent-module could support the cultivation of compassion when used in supervised, therapeutic scenarios. Fourth, we discuss the ethical implications of our analysis for user autonomy and responsibility.

show abstract

“…[7] presents a camouflaged electronic device to help potential victims of GBV; it allows to send a voice command and Global Positioning System (GPS) location via smartphone to a Control Center, which analyzes the message to properly assist the victim. A similar but more sophisticated work is presented in [8]; it uses two psychological sensors to identify GBV through a robust speaker identification system, based on the evaluation of speech stress conditions by using data augmentation techniques. Rodríguez-Rodríguez et al [9] used historic open access data to model and forecast GBV through machine learning methods; their methodology produced successful results in three specific Spanish territories with different populations.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

et al. 2021

View full text Add to dashboard Cite

The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.

show abstract

Data Augmentation for Speaker Identification under Stress Conditions to Combat Gender-Based Violence

Cited by 20 publications

References 30 publications

Editorial for Special Issue “IberSPEECH2018: Speech and Language Technologies for Iberian Languages”

Editorial for Special Issue “IberSPEECH2018: Speech and Language Technologies for Iberian Languages”

Designing Virtuous Sex Robots

Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

Contact Info

Product

Resources

About