“Application of EαNets to feature recognitionof articulation manner in knowledge-based automatic speech recognition

Speech recognition has become common in many application

domains. Incorporating acoustic-phonetic knowledge into Automatic

Speech Recognition (ASR) systems design has been proven a viable approach

to rise ASR accuracy. Manner of articulation attributes such as

vowel, stop, fricative, approximant, nasal, and silence are examples of

such knowledge. Neural networks have already been used successfully as

detectors for manner of articulation attributes starting from representations

of speech signal frames. In this paper, a set of six detectors for the

above mentioned attributes is designed based on the E-αNet model of

neural networks. This model was chosen for its capability to learn hidden

activation functions that results in better generalization properties. Experimental

set-up and results are presented that show an average 3.5%

improvement over a baseline neural network implementation.

springer06.pdf
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In  Lecture Notes in Computer Science

Publisher  Springer

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TypeInproceedings
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