Speech Communication

Top Research Topics at Speech Communication?

Speech Communication aims to foster the development of research in Speech recognition, Artificial intelligence, Speech processing, Natural language processing and Pattern recognition. Research in Speech enhancement and the interrelating topic of Noise reduction were among the subjects of interest in the Speech recognition studies discussed in it. The journal focuses on Artificial intelligence but the discussions also offer insight into other areas such as Machine learning and Prosody.

Research in Prosody tackled falls within the umbrella of Linguistics. The research on Speech processing featured in the journal combines topics in other fields like Algorithm, Speech perception, Phonetics and Speech coding. Some problems in Natural language processing that were presented in the journal overlapped with concepts under Pronunciation, Speech corpus, Vocabulary and Word (computer architecture).

Pattern recognition research is concerned with Mel-frequency cepstrum in particular. It facilitated presentations on Vowel research, particularly Formant and Consonant. The Hidden Markov model study featured in Speech Communication draws connections with the study of Markov model.

• Speech recognition (76.52%)
• Artificial intelligence (31.78%)
• Speech processing (23.67%)

What are the most cited papers published in the journal?

• Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: possible role of a repetitive structure in sounds (1666 citations)
• Assessment for automatic speech recognition II: NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems (1463 citations)
• Vocal communication of emotion: a review of research paradigms (1319 citations)

Research areas of the most cited articles at Speech Communication:

The most cited papers tackle a plethora of topics, such as Speech recognition, Speech processing, Artificial intelligence, Natural language processing and Prosody. The published articles explore research in Speech processing alongside concepts in Noise and other areas of study in Noise reduction. While Artificial intelligence is the focus of the journal papers, it also provides insights into the studies of Pronunciation and Pattern recognition.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Linguistics
• Statistics

The previous edition focused in particular on these issues:

The journal is mainly concerned with subjects like Speech recognition, Noise, Perception, Artificial neural network and Cognitive psychology. In Speech Communication, Variation (linguistics), Feature (machine learning) and Convolutional neural network are investigated in conjunction with one another to address concerns in Speech recognition research. The presentations focused mostly on Convolutional neural network in an attempt to further explore topics in Artificial intelligence.

Discussions in it are anchored in the field of Noise but it branches out to cover the subject of interrelated disciplines, including

• Active listening, which have a strong connection to Intelligibility (communication), Audiology, Cochlear implant, Sound quality and Frequency modulation,
• Notice most often made with reference to Loudness compensation.. The studies in Perception featured incorporate elements of Contrast (statistics), Irony, Breathy voice, Intonation (linguistics) and Prosody. The work on Cognitive psychology tackled in Speech Communication brings together disciplines like Affect (linguistics), Conversation and Cognition.

The most cited articles from the last journal are:

• Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson's Disease Detection (6 citations)
• Text-conditioned Transformer for automatic pronunciation error detection (5 citations)
• Learning deep multimodal affective features for spontaneous speech emotion recognition (4 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in Speech Communication (based on the number of publications) are:

• Kuldip K. Paliwal (36 papers) absent at the last edition,
• John H. L. Hansen (32 papers) published 3 papers at the last edition,
• Paavo Alku (21 papers) absent at the last edition,
• Sadaoki Furui (18 papers) absent at the last edition,
• Hervé Bourlard (17 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Speech Communication (based on the number of publications) are:

• Centre national de la recherche scientifique (61 papers) absent at the last edition,
• University of Texas at Dallas (48 papers) published 4 papers at the last edition, 3 more than at the previous edition,
• University of Edinburgh (46 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Radboud University Nijmegen (42 papers) published 2 papers at the last edition,
• University of Cambridge (35 papers) absent at the last edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 4.41% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 21.54% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.62% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.92% of all publications and 56.92% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities and Required Qualifications for Speech Communication Researchers

In the field of Speech Communication Research, there are numerous career opportunities that extend beyond academia. These roles span various industries and sectors including technology, healthcare, linguistics, artificial intelligence, and more. However, it is important to familiarize yourself with the qualifications required to advance in this professionally rewarding yet challenging field.

For instance, researchers specializing in Speech Recognition and Natural Language Processing could pursue roles as Speech Scientists, Language Processing Engineers, or Voice User Interface Designers. These careers often entail working on the cutting edge of technology, developing software and hardware solutions to advance human-to-machine communication.

Artificial Intelligence researchers have a vast array of opportunities ahead of them. From AI Specialists to Machine Learning Engineers, these professionals contribute to the expansion and refinement of smarter, more intuitive technologies.

It is worth noting that though these careers are intellectually stimulating and professionally rewarding, they require specific qualifications. The minimal requirement to enter these fields is usually a Bachelor's degree in related fields like Computer Science, Linguistics or Engineering. However, Master’s degree and PhD qualifications provide an edge, especially for roles in academia or research-heavy industries. These higher qualifications often cement an individual's expertise in their area of research, providing the foundation for thought leadership and authority.

For those interested in teaching, such as becoming a high school teacher focused on history and communication, different qualifications and certifications may be required. You can learn more about these requirements by navigating to this detailed guide on how to become a high school history teacher in new jersey.

Beyond formal education, these career paths also necessitate a range of soft and hard skills. These include programming skills, competency in AI and machine learning tools, excellent command over language and effective communication skills.

Ultimately, the field of Speech Communication Research presents a vibrant array of career options that combine elements of linguistics, technology, and cognitive psychology. By equipping themselves with the needed knowledge and skills, individuals can make valuable contributions to this evolving field whilst advancing their own professional development.