| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 718 | 17 | 28 | 7 |
International Journal of Speech Technology mainly deals with areas of study such as Speech recognition, Artificial intelligence, Natural language processing, Pattern recognition and Mel-frequency cepstrum. The journal features Speech recognition research that overlaps with concepts in Artificial neural network. The research on Artificial intelligence featured in International Journal of Speech Technology combines topics in other fields like Context (language use) and Machine learning.
It explores topics in Natural language processing which can be helpful for research in disciplines like Pronunciation, Word (computer architecture), Vocabulary and Prosody. Pattern recognition studies presented include Feature vector, Classifier (UML) and Wavelet. The Mel-frequency cepstrum works featured in International Journal of Speech Technology incorporate elements from Mixture model, Feature (machine learning) and Cepstrum.
Speaker diarisation is a key component of Speaker recognition research discussed in it.
The journal papers are organized to reinforce research efforts on Speech recognition, Artificial intelligence, Natural language processing, Pattern recognition and Speaker recognition. The published articles explore issues in Speech recognition which can be linked to other research areas like Mixture model, Artificial neural network and Mel-frequency cepstrum. The study of Artificial intelligence in the journal papers encompasses disciplines such as Vocabulary, as well as fields such as Spelling and Human–computer interaction, all of which overlap with one another.
Speech recognition, Artificial intelligence, Artificial neural network, Deep learning and Convolutional neural network are the subjects of interest in International Journal of Speech Technology. Some problems in Speech recognition that were presented in the journal overlapped with concepts under Signal and Mel-frequency cepstrum. International Journal of Speech Technology connects the study in Mel-frequency cepstrum with the closely related area of Feature (machine learning).
In International Journal of Speech Technology, Natural language processing, Machine learning and Pattern recognition are investigated in conjunction with one another to address concerns in Artificial intelligence research. While the journal focused on Natural language processing, it was also able to explore topics like Context (language use) and Word (computer architecture). It concentrates on Pattern recognition topics that focus on Principal component analysis and Support vector machine.
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.
The top authors publishing in International Journal of Speech Technology (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in International Journal of Speech Technology (based on the number of publications) are:
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.
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, 23.02% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 26.17% were posted by at least one author from the top 10 institutions publishing in the journal. Another 3.74% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.15% of all publications and 57.94% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
In addition to research and academia, the field of speech technology offers various career opportunities, particularly in education. For instance, proficiency in speech technology could open doors for a career as an English teacher in many states, including Florida. This role would involve utilizing technology to enhance language learning, using tools for speech recognition and Natural Language Processing (NLP).
Taking this diverse and interdisciplinary field of study can lead to a teaching career, where language acquisition and technology intersect, creating innovative teaching approaches. To understand more about this, potential career seekers are encouraged to read up on requirements to become an English teacher in Florida.
This intersectional career path is becoming increasingly appealing as classrooms around the globe employ modern technological tools for teaching. Furthermore, the ability to combine a traditional role like teaching with innovative technological skills opens the door to numerous possibilities.
With a deeper understanding of Speech Technology, future English teachers will be able to harness cutting-edge technologies, enhancing their instruction methods and providing their students with an enriched learning experience.
Nabanita Das;Sayan Chakraborty;Jyotismita Chaki;Neelamadhab Padhy
(2021)Samia Abd El-Moneim;Ahmed Sedik;M. A. Nassar;Adel S. El-Fishawy
(2021)Ismail Shahin;Ali Bou Nassif;Noor Ahmad Al Hindawi
(2021)Samia Abd El-Moneim;El-Sayed El-Rabaie;Mohamed Abd-Elsalam Nassar;Moawad I. Dessouky
(2020)Wei Wang;Paul A. Watters;Xinyi Cao;Lingjie Shen
(2020)Bidhan Barai;Tapas Chakraborty;Nibaran Das;Subhadip Basu
(2021)Hossam Hammam;Walid El-Shafai;Emad Hassan;Atef E. Abu El-Azm
(2021)Emilia Parada-Cabaleiro;Anton Batliner;Alice Baird;Björn W. Schuller;Björn W. Schuller
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