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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English

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Silva, Diego Furtado [1] ; Alves de Souza, Vinicius Mourao [1] ; Prado Alves Batista, Gustavo Enrique de Almeida [1]
Total Authors: 3
[1] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, BR-13566590 Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Source: ACTA SCIENTIARUM-TECHNOLOGY; v. 35, n. 4, p. 621-628, 2013.
Web of Science Citations: 2

Recognition of isolated spoken digits is the core procedure for a large number of applications which rely solely on speech for data exchange, as in telephone-based services, such as dialing, airline reservation, bank transaction and price quotation. Spoken digit recognition is generally a challenging task since the signals last for a short period of time and often some digits are acoustically very similar to other digits. The objective of this paper is to investigate the use of machine learning algorithms for spoken digit recognition and disclose the free availability of a database with digits pronounced in English and Portuguese to the scientific community. Since machine learning algorithms are fully dependent on predictive attributes to build precise classifiers, we believe that the most important task for successfully recognizing spoken digits is feature extraction. In this work, we show that Line Spectral Frequencies (LSF) provide a set of highly predictive coefficients. We evaluated our classifiers in different settings by altering the sampling rate to simulate low quality channels and varying the number of coefficients. (AU)

FAPESP's process: 12/07295-3 - Complexity-invariance for classification, clustering and motif discovery in time series
Grantee:Gustavo Enrique de Almeida Prado Alves Batista
Support type: Regular Research Grants
FAPESP's process: 12/50714-7 - Intelligent sensor for controlling agricultural pests and disease-vector insects
Grantee:Gustavo Enrique de Almeida Prado Alves Batista
Support type: Regular Research Grants
FAPESP's process: 11/17698-5 - Classification of non-stationary data stream with application in sensors for insect identification
Grantee:Vinícius Mourão Alves de Souza
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 11/04054-2 - Time series classification by similarity and extraction of intrinsic attributes with application to insect species identification
Grantee:Diego Furtado Silva
Support type: Scholarships in Brazil - Master