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4. Speech recognition. Phonetically motivated acoustic parameters for continuous speech recognition using artificial neural networks.
The design of these networks was inspired by acoustic-phonetic knowledge. Input parameters, ANN topology, and desired output representation have been optimized ...
In the framework of an ANN/HMM hybrid system for phone recognition three specialized ANNs were designed and evaluated. One of these ANNs detects the manner of ...
May 16, 1989 · Spectrogram reading is used only as a paradigm to demonstrate the importance of utilizing acoustic-phonetic knowledge in speech recognition ...
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Phonetically motivated acoustic parameters for continuous speech recognition using artificial neural networks. In Proceedings of EuroSpeech'91, 1991. Google ...
We report work on the first component of a two stage speech recognition architecture based on phonological features rather than phones.
ABSTRACT. Studies have shown that articulatory information helps model speech variability and, consequently, improves speech recognition performance.
We attempt to combine neural networks with knowledge from speech science to build a speaker independent speech recogni- tion system.
Phonetically motivated acoustic parameters for continuous speech recognition using artificial neural networks · Yoshua BengioR. MoriG. FlammiaR. Kompe.
Mar 3, 2021 · In this paper, we compare the performance of voice recognition using Hidden Markov models (HMM) in Deep Neural Networks (DNN) and Fuzzy Logic.