SPEECH COMMUNICATION

Scope & Guideline

Innovating Research in Human Interaction and AI.

Introduction

Welcome to the SPEECH COMMUNICATION information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of SPEECH COMMUNICATION, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN0167-6393
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1982 to 2024
AbbreviationSPEECH COMMUN / Speech Commun.
Frequency9 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal 'Speech Communication' primarily aims to publish high-quality research that advances the understanding and application of speech communication technologies and sciences. It encompasses a wide array of topics related to speech processing, recognition, and synthesis, with a particular emphasis on interdisciplinary approaches that integrate insights from linguistics, engineering, computer science, and cognitive psychology.
  1. Speech Recognition and Processing:
    Research focusing on algorithms and techniques for automatic speech recognition (ASR), including advancements in neural network architectures and machine learning models.
  2. Speech Synthesis:
    Studies related to generating human-like speech from text, encompassing various methodologies including deep learning, concatenative synthesis, and waveform generation.
  3. Speech Quality Assessment:
    Investigations into metrics and models for evaluating speech quality, including non-intrusive methods and the impact of various noise conditions on intelligibility.
  4. Emotion and Speaker Recognition:
    Exploration of emotional cues in speech, techniques for emotion recognition, and methods for identifying speakers based on vocal characteristics.
  5. Multimodal Speech Processing:
    Research integrating audio with visual or textual information to enhance speech understanding and recognition, particularly in challenging environments.
  6. Speech Disorders and Rehabilitation:
    Studies aimed at understanding and improving speech production and recognition for individuals with speech disorders, including dysarthria and other conditions.
  7. Cross-linguistic and Dialectal Studies:
    Examination of speech characteristics across different languages and dialects, focusing on phonetic, phonological, and prosodic features.
  8. Applications of Speech Technology:
    Practical applications of speech technologies in various fields such as healthcare, education, and human-computer interaction, aiming to solve real-world problems.
The journal has recently observed a rise in interest in several innovative and interdisciplinary research themes, reflecting the evolving landscape of speech communication technology and its applications.
  1. Self-supervised Learning in Speech Processing:
    There is a growing trend towards self-supervised learning techniques that require less labeled data while still achieving high performance in tasks like speech recognition and emotion detection.
  2. Integration of AI and Speech Technologies:
    The intersection of artificial intelligence with speech technology is becoming increasingly prominent, with research exploring AI-driven enhancements in speech recognition and synthesis.
  3. Speech Emotion Recognition and Affective Computing:
    A surge in interest around recognizing emotions from speech signals, with applications in healthcare, customer service, and human-computer interaction.
  4. Multimodal Interaction Systems:
    Emerging research is focusing on systems that combine audio with visual or haptic feedback to improve communication effectiveness in various settings.
  5. Voice Conversion and Style Transfer:
    Research into voice conversion techniques and the ability to transfer vocal styles has gained traction, particularly in creative applications and entertainment.
  6. Neural Network Architectures for Speech Applications:
    The development and comparison of novel neural network architectures specifically tailored for speech applications is increasingly prevalent, showcasing advancements in deep learning.
  7. Robustness in Speech Recognition:
    Research aimed at improving the robustness of speech recognition systems in noisy or variable environments is becoming a key focus, addressing real-world challenges.

Declining or Waning

Over recent years, certain research areas within 'Speech Communication' have seen a decline in publication frequency or prominence. This shift may reflect changing priorities within the field or the saturated nature of specific topics.
  1. Traditional Acoustic Modeling Techniques:
    Research focused on classical statistical modeling approaches for speech recognition has decreased as deep learning techniques have become dominant.
  2. Low-level Signal Processing:
    Studies that primarily concentrate on low-level signal processing methods, such as basic filtering and spectral analysis, appear to be waning in favor of more integrated and high-level approaches.
  3. Basic Phonetics and Phonology:
    While foundational phonetic and phonological studies remain important, there seems to be a shift towards applied and computational aspects, leading to fewer publications in purely theoretical phonetics.
  4. Static Analysis of Speech Features:
    Research that relies heavily on static feature extraction without considering dynamic or contextual factors is becoming less prevalent, as the focus shifts to more adaptive and context-aware approaches.

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