ROBUST SPEECH RECOGNITION USING TEMPORAL PATTERN FEATURE EXTRACTED FROM MTMLP STRUCTURE

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

SHEKOFTEH YASSER; Almasganj farshad

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2014 | Volume:5 | Issue:3 Page(s): 23-35

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

910

Download:

Cites:

Information Journal Paper

Title

ROBUST SPEECH RECOGNITION USING TEMPORAL PATTERN FEATURE EXTRACTED FROM MTMLP STRUCTURE

Author(s)

SHEKOFTEH YASSER | Almasganj farshad | Issue Writer Certificate

Keywords

SPEECH RECOGNITIONQ2

FEATURE EXTRACTIONQ2

TEMPORAL PATTERNQ2

POSTERIOR PROBABILITYQ2

NEURAL NETWORKQ2

HIDDEN MARKOV MODELQ2

Abstract

Temporal Pattern feature of a speech signal could be either extracted from the time domain or via their front-end vectors. This feature includes long-term information of variations in the connected speech units. In this paper, the second approach is followed, i.e. the features which are the cases of temporal computations, consisting of Spectral-based (LFBE) and Cepstrum-based (MFCC) feature vectors, are considered. To extract these features, we use POSTERIOR PROBABILITY-based output of the proposed MTMLP NEURAL NETWORKs. The combination of the TEMPORAL PATTERNs, which represents the long-term dynamics of the speech signal, together with some traditional features, composed of the MFCC and its first and second derivatives are evaluated in an ASR task. It is shown that the use of such a combined feature vector results in the increase of the phoneme recognition accuracy by more than 1 percent regarding the results of the baseline system, which does not benefit from the long-term TEMPORAL PATTERNs. In addition, it is shown that the use of extracted features by the proposed method gives robust recognition under different noise conditions (by 13 percent) and, therefore, the proposed method is a robust FEATURE EXTRACTION method.

Cites

No record.

References

No record.

Cite

APA: Copy

SHEKOFTEH, YASSER, & Almasganj, farshad. (2014). ROBUST SPEECH RECOGNITION USING TEMPORAL PATTERN FEATURE EXTRACTED FROM MTMLP STRUCTURE. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 5(3), 23-35. SID. https://sid.ir/paper/202941/en

Vancouver: Copy

SHEKOFTEH YASSER, Almasganj farshad. ROBUST SPEECH RECOGNITION USING TEMPORAL PATTERN FEATURE EXTRACTED FROM MTMLP STRUCTURE. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2014;5(3):23-35. Available from: https://sid.ir/paper/202941/en

IEEE: Copy

YASSER SHEKOFTEH, and farshad Almasganj, “ROBUST SPEECH RECOGNITION USING TEMPORAL PATTERN FEATURE EXTRACTED FROM MTMLP STRUCTURE,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 5, no. 3, pp. 23–35, 2014, [Online]. Available: https://sid.ir/paper/202941/en

Related Journal Papers