Paper Info
Title
Speech recognition using energy 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 parameter increases speech recognition by 5% when compared with recognition using STTEF in discontinuous speech and improved more than 1.5% in continuous speech with three states. When the number of states is incremented to five, the recognition rate is improved proportionally to 97.5% for the discontinuous speech and to 80.5% for the continuous one.
Year
DOI
Venue
2005
10.1007/11578079_18
CIARP
Keywords
Field
DocType
speech recognition,ero parameter increases speech,speech signal,automatic speech re-cognition,energy parameter,ero parameter,recognition rate,short-term total energy function,energy function,continuous speech,natural speech,spanish language,discontinuous speech,modeling language,high frequency
Syllabic verse,Audio mining,Computer science,Speech recognition,Natural language,Bigram,Syllable,Hidden Markov model,Linear predictive coding,Acoustic model
Conference
Volume
ISSN
ISBN
3773
0302-9743
3-540-29850-9
Citations 
PageRank 
References 
0
0.34
5
Authors
4
Name
Order
Citations
PageRank
Sergio Suárez-Guerra1378.81
José Luis Oropeza Rodríguez256.49
Edgardo M. Felipe-Riverón35212.61
Jesús Figueroa Nazuno400.68