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.
Keywords
- Speech Recognition
- Speech Signal
- Inference Rule
- Automatic Speech Recognition
- Spanish Language
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2005 Springer-Verlag Berlin Heidelberg
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Guerra, S.S., Rodríguez, J.L.O., Riveron, E.M.F., Nazuno, J.F. (2005). Speech Recognition Using Energy Parameters to Classify Syllables in the Spanish Language. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_18
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DOI: https://doi.org/10.1007/11578079_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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