Paper Info
Title
Speech recognition using energy, MFCCs and rho parameters to classify syllables in the spanish language
Abstract
This paper presents an approach for the automatic speech re-cognition using syllabic units. Its segmentation is based on using the Short-Term Total Energy Function (STTEF) and the Energy Function of the High Frequency (ERO parameter) higher than 3,5 KHz of the speech signal. Training for the classification of the syllables is based on ten related Spanish language rules for syllable splitting. Recognition is based on a Continuous Density Hidden Markov Models and the bigram model language. The approach was tested using two voice corpus of natural speech, one constructed for researching in our laboratory (experimental) and the other one, the corpus Latino40 commonly used in speech researches. The use of ERO and MFCCs parameter increases speech recognition by 5.5% when compared with recognition using STTEF in discontinuous speech and improved more than 2% in continuous speech with three states. When the number of states is incremented to five, the recognition rate is improved proportionally to 98% for the discontinuous speech and to 81% for the continuous one.
Year
DOI
Venue
2006
10.1007/11925231_101
MICAI
Keywords
Field
DocType
speech signal,automatic speech re-cognition,energy function,continuous speech,natural speech,discontinuous speech,spanish language,speech recognition,rho parameter,mfccs parameter increases speech,recognition rate,ero parameter,short-term total energy function,high frequency,modeling language
Syllabic verse,Segmentation,Computer science,Audio mining,Speech recognition,Natural language,Syllable,Bigram,Hidden Markov model,Acoustic model
Conference
Volume
ISSN
ISBN
4293
0302-9743
3-540-49026-4
Citations 
PageRank 
References 
0
0.34
5
Authors
4
Name
Order
Citations
PageRank
Sergio Suárez-Guerra1378.81
José Luis Oropeza Rodríguez256.49
Edgardo Manuel Felipe Riveron300.34
Jesús Figueroa Nazuno400.68