Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Mikel K. Ngueajio

dblp:328/1043 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0002-9696-2372ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Trustworthy machine learning · 67% Speech recognition and synthesis · 33%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
cross-language information retrieval
1.012026
The Alignment Gap: A Benchmark Demonstrating the Lack of Cross-Lingual Mapping in Dialect-Specialized Language Models - The Case of Ehugbo · SIGIR 2026
Natural language and speech › Speech recognition and synthesis
automatic speech recognition
0.712023
Hey, Siri! Why Are You Biased against Women? (Student Abstract) · AAAI 2023
Machine learning › Trustworthy machine learning › fairness
demographic bias
0.712023
Hey, Siri! Why Are You Biased against Women? (Student Abstract) · AAAI 2023
Machine learning › Trustworthy machine learning
fairness
0.712023
Hey, Siri! Why Are You Biased against Women? (Student Abstract) · AAAI 2023

Methods — techniques the papers use, named apart from their topics

systematic literature review · 1.3benchmark · 1.0
YearPublicationVenuePosition
2026 The Alignment Gap: A Benchmark Demonstrating the Lack of Cross-Lingual Mapping in Dialect-Specialized Language Models - The Case of Ehugbo
Ukachi Agnes Eze-Mbey, Victor Olufemi, Athanase Biluge Bahizire, Mikel K. Ngueajio, Prasenjit Mitra 0001
SIGIR4
2023 Hey, Siri! Why Are You Biased against Women? (Student Abstract)
abstract
The intersection of pervasive technology and verbal communication has resulted in the creation of Automatic Speech Recognition Systems (ASRs), which automate the conversion of spontaneous speech into texts. ASR enables human-computer interactions through speech and is rapidly integrated into our daily lives. However, the research studies on current ASR technologies have reported unfulfilled social inclusivity and accentuated biases and stereotypes towards minorities. In this work, we provide a review of examples and evidence to demonstrate preexisting sexist behavior in ASR systems through a systematic review of research literature over the past five years. For each article, we also provide the ASR technology used, highlight specific instances of reported bias, discuss the impact of this bias on the female community, and suggest possible methods of mitigation. We believe this paper will provide insights into the harm that unchecked AI-powered technologies can have on a community by contributing to the growing body of research on this topic and underscoring the need for technological inclusivity for all demographics, especially women.
Surakshya Aryal, Mikel K. Ngueajio, Saurav K. Aryal, Gloria J. Washington
AAAI2