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Speech Recognition and Natural Language Processing—Second Edition

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 August 2026 | Viewed by 437

Special Issue Editors

Department of Computing and Mathematics, Faculty of Science and Engineering, University of Derby, Derby DE22 1GB, UK
Interests: artificial intelligence (AI); natural language processing (NLP)
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computing, University of Derby, Derby DE22 3AW, UK
Interests: data science; artificial intelligence; sentiment analysis; natural language processing; medical image classification using machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest advancements and challenges in the interdisciplinary fields of speech recognition (SR) and natural language processing (NLP), which have emerged as two of the most transformative fields in artificial intelligence. As the demand for intelligent systems capable of understanding and processing human language continues to rise, researchers are increasingly focusing on developing innovative algorithms, models, and applications in these domains. This Special Issue provides a platform for scholars and practitioners to disseminate their cutting-edge research findings, methodologies, and insights, fostering collaboration and driving progress in this dynamic field.

Topics of interest include, but are not limited to, the following:

  • Automatic speech recognition (ASR) systems;
  • Natural language understanding (NLU) and interpretation;
  • Speech synthesis and generation;
  • Sentiment analysis and opinion mining;
  • Dialogue systems and conversational interfaces;
  • Machine translation and cross-lingual NLP;
  • Voice user interfaces (VUIs) and intelligent assistants;
  • Language modelling and representation learning;
  • End-to-end speech-to-text and text-to-speech systems;
  • Speech and language applications.

We invite original research contributions, review articles, case studies, and surveys that advance the state of the art in speech recognition and natural language processing. Submissions should present novel methodologies, experimental results, theoretical insights, or practical applications that contribute to the development and understanding of these critical areas.

Dr. Asad Abdi
Prof. Dr. Farid Meziane
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • automatic speech recognition
  • natural language understanding
  • sentiment analysis
  • machine translation
  • voice user interfaces
  • speech-to-text
  • text-to-speech
  • dialogue systems
  • conversational AI
  • spoken language understanding
  • language modelling

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Published Papers (1 paper)

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Research

17 pages, 4532 KB  
Article
Ranked Multi-Label-Augmented Topic Modeling for Legislative Content Profiling
by Francesco Invernici, Andrea Colombo, Flaminia Telese and Anna Bernasconi
Appl. Sci. 2026, 16(9), 4383; https://doi.org/10.3390/app16094383 - 30 Apr 2026
Viewed by 157
Abstract
Navigating extensive legislative corpora is often impeded by the linguistic complexity inherent in legal texts. To address this, we present a novel topic representation learning method designed to facilitate the systematic exploration of legislative content. We demonstrate the efficacy of this approach by [...] Read more.
Navigating extensive legislative corpora is often impeded by the linguistic complexity inherent in legal texts. To address this, we present a novel topic representation learning method designed to facilitate the systematic exploration of legislative content. We demonstrate the efficacy of this approach by applying it to the vast corpus of Italian legislation comprising about 74 k laws with more than 300 k articles. While current topic models group documents by latent semantic similarity, they often lack the granularity required for precise navigation. Our approach augments these representations by integrating our topic modeling framework with multi-label profiles. We enrich the representation of individual laws by extracting and ranking the top 10 keywords based on their relevance to the enclosing topic, subsequently aggregating these rankings to construct a comprehensive, alternative description of the broader legal themes. By bridging latent semantic clusters with explicit, LLM-generated labels, this method yields a highly interpretable representation of the corpus, significantly enhancing the profiling and navigability of complex legislative content. We improve over our baseline representation in 74.67% of cases, showing potential for re-use in highly specialized text corpora. Full article
(This article belongs to the Special Issue Speech Recognition and Natural Language Processing—Second Edition)
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