TY - GEN
T1 - Speech recognition using energy, MFCCs and rho parameters to classify syllables in the Spanish language
AU - Guerra, Sergio Suárez
AU - Rodriguez, José Luis Oropeza
AU - Riveron, Edgardo Manuel Felipe
AU - Nazuno, Jesús Figueroa
PY - 2006
Y1 - 2006
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 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.
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 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.
UR - http://www.scopus.com/inward/record.url?scp=33845943766&partnerID=8YFLogxK
U2 - 10.1007/11925231_101
DO - 10.1007/11925231_101
M3 - Contribución a la conferencia
SN - 3540490264
SN - 9783540490265
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1057
EP - 1066
BT - MICAI 2006
PB - Springer Verlag
T2 - 5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence
Y2 - 13 November 2006 through 17 November 2006
ER -