Example of International Journal of Speech Technology format
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Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format
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Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format Example of International Journal of Speech Technology format
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open access Open Access ISSN: 13812416 e-ISSN: 15728110
recommended Recommended

International Journal of Speech Technology — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Language and Linguistics #70 of 879 up up by 68 ranks
Linguistics and Language #81 of 935 up up by 75 ranks
Computer Vision and Pattern Recognition #47 of 85 up up by 3 ranks
Human-Computer Interaction #67 of 120 up up by 1 rank
Software #228 of 389 up up by 47 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 341 Published Papers | 942 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 18/07/2020
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Journal Performance & Insights

  • CiteRatio
  • SJR
  • SNIP

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

2.8

27% from 2019

CiteRatio for International Journal of Speech Technology from 2016 - 2020
Year Value
2020 2.8
2019 2.2
2018 1.6
2017 1.3
2016 1.4
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 27% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

0.203

17% from 2019

SJR for International Journal of Speech Technology from 2016 - 2020
Year Value
2020 0.203
2019 0.244
2018 0.222
2017 0.22
2016 0.196
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 17% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

0.959

14% from 2019

SNIP for International Journal of Speech Technology from 2016 - 2020
Year Value
2020 0.959
2019 0.84
2018 1.183
2017 0.875
2016 1.033
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has increased by 14% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

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CiteRatio: 4.8 | SJR: 0.459 | SNIP: 1.587
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CiteRatio: 3.8 | SJR: 0.29 | SNIP: 1.153
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International Journal of Speech Technology

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Springer

International Journal of Speech Technology

The International Journal of Speech Technology is a research journal that focuses on speech technology and its applications. It promotes research and description on all aspects of speech input and output, including theory, experiment, testing, base technology, applications.The...... Read More

Language and Linguistics

Linguistics and Language

Computer Vision and Pattern Recognition

Human-Computer Interaction

Software

Arts and Humanities

i
Last updated on
18 Jul 2020
i
ISSN
1381-2416
i
Impact Factor
Medium - 0.925
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1023/A:1025708916924
The German Text-to-Speech Synthesis System MARY: A Tool for Research, Development and Teaching
Marc Schröder, Jürgen Trouvain1

Abstract:

This paper introduces the German text-to-speech synthesis system MARY. The system's main features, namely a modular design and an XML-based system-internal data representation, are pointed out, and the properties of the individual modules are briefly presented. An interface allowing the user to access and modify intermediate ... This paper introduces the German text-to-speech synthesis system MARY. The system's main features, namely a modular design and an XML-based system-internal data representation, are pointed out, and the properties of the individual modules are briefly presented. An interface allowing the user to access and modify intermediate processing steps without the need for a technical understanding of the system is described, along with examples of how this interface can be put to use in research, development and teaching. The usefulness of the modular and transparent design approach is further illustrated with an early prototype of an interface for emotional speech synthesis. read more read less

Topics:

Modular design (54%)54% related to the paper, Speech synthesis (53%)53% related to the paper, Interface (Java) (53%)53% related to the paper, XML (52%)52% related to the paper
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443 Citations
Journal Article DOI: 10.1007/S10772-011-9125-1
Emotion recognition from speech: a review
Shashidhar G. Koolagudi1, K. Sreenivasa Rao1

Abstract:

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related ... Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related to emotional speech corpora, different types of speech features and models used for recognition of emotions from speech. Thirty two representative speech databases are reviewed in this work from point of view of their language, number of speakers, number of emotions, and purpose of collection. The issues related to emotional speech databases used in emotional speech recognition are also briefly discussed. Literature on different features used in the task of emotion recognition from speech is presented. The importance of choosing different classification models has been discussed along with the review. The important issues to be considered for further emotion recognition research in general and in specific to the Indian context have been highlighted where ever necessary. read more read less

Topics:

Speech processing (71%)71% related to the paper, Speech corpus (68%)68% related to the paper, Speaker recognition (59%)59% related to the paper, TRACE (psycholinguistics) (56%)56% related to the paper
View PDF
414 Citations
Journal Article DOI: 10.1007/S10772-018-9491-Z
Databases, features and classifiers for speech emotion recognition: a review
Monorama Swain1, Aurobinda Routray2, P. Kabisatpathy3

Abstract:

Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many r... Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person's speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages. read more read less

Topics:

Speech corpus (67%)67% related to the paper
114 Citations
Journal Article DOI: 10.1007/S10772-012-9172-2
Emotion recognition from speech using global and local prosodic features
K. Sreenivasa Rao1, Shashidhar G. Koolagudi1, Ramu Reddy Vempada1

Abstract:

In this paper, global and local prosodic features extracted from sentence, word and syllables are proposed for speech emotion or affect recognition. In this work, duration, pitch, and energy values are used to represent the prosodic information, for recognizing the emotions from speech. Global prosodic features represent the ... In this paper, global and local prosodic features extracted from sentence, word and syllables are proposed for speech emotion or affect recognition. In this work, duration, pitch, and energy values are used to represent the prosodic information, for recognizing the emotions from speech. Global prosodic features represent the gross statistics such as mean, minimum, maximum, standard deviation, and slope of the prosodic contours. Local prosodic features represent the temporal dynamics in the prosody. In this work, global and local prosodic features are analyzed separately and in combination at different levels for the recognition of emotions. In this study, we have also explored the words and syllables at different positions (initial, middle, and final) separately, to analyze their contribution towards the recognition of emotions. In this paper, all the studies are carried out using simulated Telugu emotion speech corpus (IITKGP-SESC). These results are compared with the results of internationally known Berlin emotion speech corpus (Emo-DB). Support vector machines are used to develop the emotion recognition models. The results indicate that, the recognition performance using local prosodic features is better compared to the performance of global prosodic features. Words in the final position of the sentences, syllables in the final position of the words exhibit more emotion discriminative information compared to the words and syllables present in the other positions. read more read less

Topics:

Speech corpus (56%)56% related to the paper, Prosody (54%)54% related to the paper
114 Citations
Journal Article DOI: 10.1023/A:1025700715107
Audiovisual Speech Synthesis
Gérard Bailly1, Maxime Berar1, Frédéric Elisei1, Matthias Odisio1

Abstract:

This paper presents the main approaches used to synthesize talking faces, and provides greater detail on a handful of these approaches. An attempt is made to distinguish between facial synthesis itself (i.e. the manner in which facial movements are rendered on a computer screen), and the way these movements may be controlled ... This paper presents the main approaches used to synthesize talking faces, and provides greater detail on a handful of these approaches. An attempt is made to distinguish between facial synthesis itself (i.e. the manner in which facial movements are rendered on a computer screen), and the way these movements may be controlled and predicted using phonetic input. The two main synthesis techniques (model-based vs. image-based) are contrasted and presented by a brief description of the most illustrative existing systems. The challenging issues—evaluation, data acquisition and modeling—that may drive future models are also discussed and illustrated by our current work at ICP. read more read less

Topics:

Computer facial animation (58%)58% related to the paper
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100 Citations
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International Journal of Speech Technology format uses SPBASIC citation style.

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Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the International Journal of Speech Technology guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the International Journal of Speech Technology citation style.

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Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per International Journal of Speech Technology's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

The 5 most common citation types in order of usage are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
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Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in International Journal of Speech Technology Endnote style, according to springer guidelines.

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