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Advanced Human–AI Interaction: Speech and Natural Language Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 2793

Special Issue Editor


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Guest Editor
Computer Science Department, University of Alcala, 28801 Alcalá de Henares, Spain
Interests: machine learning; simulation methods; computational electromagnetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue highlights recent progress in artificial intelligence, focusing on applications in Natural Language Processing (NLP) with an emphasis on Speech Recognition. It brings together novel ideas and empirical findings, addressing both theoretical concepts and real-world implementations. Topics include the use of artificial intelligence, machine learning, and deep learning to analyze large-scale data from sources like satellites, scientific research, sensor networks, and medical diagnostics. A key component is the comparison of deep learning models, evaluating their efficiency, accuracy, generalizability, and interpretability across varied datasets and scenarios. The aim is to advance understanding and promote practical deployment of these technologies.

Dr. Carlos Delgado
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • artificial intelligence
  • natural language processing
  • speech recognition

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Published Papers (2 papers)

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Research

17 pages, 1147 KB  
Article
Personalized AI-Directed Tutoring for Oral Proficiency Enhancement in Language Education
by Pranav Tushar, Bowen Zhang, Indriyati Atmosukarto, Donny Soh, Rong Tong and Ian McLoughlin
Appl. Sci. 2026, 16(5), 2379; https://doi.org/10.3390/app16052379 - 28 Feb 2026
Viewed by 976
Abstract
Generative AI offers transformative potential for scalable, personalized, and dynamic language education, particularly in enhancing oral proficiency among young learners. However, effective deployment remains challenging due to limited resources for some languages, the need for age-appropriate content and tools, and the importance of [...] Read more.
Generative AI offers transformative potential for scalable, personalized, and dynamic language education, particularly in enhancing oral proficiency among young learners. However, effective deployment remains challenging due to limited resources for some languages, the need for age-appropriate content and tools, and the importance of respecting cultural relevance. In this paper, we introduce LEARN (Language Evaluation via question Answer generation from caRtooNs), a culturally grounded multilingual visual dialogue system designed to support oral proficiency in three of Singapore’s official languages: Mandarin, Bahasa Melayu, and Tamil. English, as the lingua franca, is excluded. LEARN integrates a teacher-facing module for curriculum-aligned visual question-answering task creation and a student-facing module for voice-driven adaptive dialogue, optimized for children’s speech. Unlike existing platforms, LEARN prioritizes cultural relevance and low-resource language support, helping address gaps in heritage language preservation. Pilot studies with students demonstrate significant improvements in engagement and vocabulary acquisition. Designed for classroom as well as home use, LEARN presents a scalable AI-driven language tutoring framework. Full article
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14 pages, 1549 KB  
Article
Temporal Dynamics of Harmful Speech in Chatbot–User Dialogues: A Comparative Study of LLM and Chit-Chat Systems
by Ohseong Kwon, Hyobeen Yoon, Hyojin Chin and Jisung Park
Appl. Sci. 2025, 15(24), 13185; https://doi.org/10.3390/app152413185 - 16 Dec 2025
Viewed by 1427
Abstract
Harmful language in conversational AI poses distinct safety and governance challenges, as Large Language Model (LLM) chatbots interact in private, one-to-one settings. Understanding the types of harm and their temporal concentration is crucial for responsible deployment and time-aware moderation. This study investigates the [...] Read more.
Harmful language in conversational AI poses distinct safety and governance challenges, as Large Language Model (LLM) chatbots interact in private, one-to-one settings. Understanding the types of harm and their temporal concentration is crucial for responsible deployment and time-aware moderation. This study investigates the types and diurnal dynamics of harmful speech, comparing patterns between play-oriented chit-chat and task-oriented LLM services.We analyze two large-scale, real-world English corpora: a chit-chat service (SimSimi; 8.7 M utterances) and an LLM service (WildChat; 610 K utterances). Using the Perspective API for multi-label classification (Toxicity, Profanity, Insult, Identity Attack, Threat), we estimate the incidence of harm categories and compare their distribution across five dayparts. Our analysis shows that harmful speech is significantly more prevalent in the chit-chat context than in the LLM service. Across both platforms, Toxicity and Profanity are the dominant categories. Temporally, harmful speech concentrates most frequently during the dawn daypart. We contribute an empirical baseline on how harm varies by chatbot modality and time of day, offering practical guidance for designing dynamic, platform-specific moderation policies. Full article
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