TY - GEN
T1 - Speech recognition using energy parameters to classify syllables in the Spanish language
AU - Guerra, Sergio Suárez
AU - Rodríguez, José Luis Oropeza
AU - Riveron, Edgardo M.Felipe
AU - Nazuno, Jesús Figueroa
PY - 2005
Y1 - 2005
N2 - This paper presents an approach for the automatic speech recognition 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.
AB - This paper presents an approach for the automatic speech recognition 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.
UR - http://www.scopus.com/inward/record.url?scp=33745367125&partnerID=8YFLogxK
U2 - 10.1007/11578079_18
DO - 10.1007/11578079_18
M3 - Contribución a la conferencia
SN - 3540298509
SN - 9783540298502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 170
BT - Progress in Pattern Recognition, Image Analysis and Applications - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Proceedings
T2 - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005
Y2 - 15 November 2005 through 18 November 2005
ER -