Fundamentals of speech recognition
Fundamentals of speech recognition
What is the goal of sensory coding?
Neural Computation
Challenges in adopting speech recognition
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
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This paper shows results obtained in the Automatic Speech Recognition (ASR) task for a corpus of digits speech files with a determinate noise level immerse. In the experiments, we used several speech files that contained Gaussian noise. We used HTK (Hidden Markov Model Toolkit) software of Cambridge University in the experiments. The noise level added to the speech signals was varying from fifteen to forty dB increased by a step of 5 units. We used an adaptive filtering to reduce the level noise (it was based in the Least Measure Square -LMS- algorithm) and two different wavelets (Haar and Daubechies). With LMS we obtained an error rate lower than if it was not present and it was better than wavelets employed for this experiment of Automatic Speech Recognition. For decreasing the error rate we trained with 50% of contaminated and originals signals to the ASR system. The results showed in this paper are focused to try analyses the ASR performance in a noisy environment and to demonstrate that if we are controlling the noise level and if we know the application where it is going to work, then we can obtain a better response in the ASR tasks. Is very interesting to count with these results because speech signal that we can find in a real experiment (extracted from an environment work, i.e.), could be treated with these technique and we can decrease the error rate obtained. Finally, we report a recognition rate of 99%, 97.5% 96%, 90.5%, 81% and 78.5% obtained from 15, 20, 25, 30, 35 and 40 noise levels, respectively when the corpus mentioned before was employed and LMS algorithm was used. Haar wavelet level 1 reached up the most important results as an alternative to LMS algorithm, but only when the noise level was 40 dB and using original corpus.