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Abstract
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| Neural networks (NNs) have been extensively used in speech technology systems. In this paper, we present two novel applications of NNs in speech recognition and text-to-speech systems. In very large vocabulary speech recognition systems using the hypothesis-verification paradigm, the verification stage is usually the most time consuming. State of the art systems combine fixed size hypothesized search spaces with advanced pruning techniques. We propose a novel strategy to dynamically calculate the hypothesized search space, using neural networks as the estimation module and designing the input feature set with a careful greedy-based selection approach. The main achievement has been a statistically significant relative decrease in error rate of 33.53%, while getting a relative decrease in average computational demands of up to 19.40%. The prosodic modeling is one of the most important tasks for developing a new text-to-speech synthesizer, especially in a female-voice high-quality restricted-domain system. Our double objective is to get accurate predictors for both the fundamental frequency (F0) curve and phoneme duration by minimizing the model estimation error in a Spanish text-to-speech system, by means of a neural network estimator, which has proved to be an excellent tool for the modeling. The resulting system predicts prosody with very good results (for duration: 15.5 ms in RMS and a correlation factor of 0.8975; for F0: 19.80 Hz in RMS and a relative RMS error of 0.43) that clearly improves our previous rule-based system. | |
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International
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Si |
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JCR
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Si |
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Title
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INTELLIGENT AUTOMATION AND SOFT COMPUTING |
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ISBN
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1079-8587 |
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Impact factor JCR
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0,224 |
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Impact info
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Volume
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15 |
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Journal number
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4 |
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From page
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631 |
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To page
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646 |
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Month
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ENERO |
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Ranking
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