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.

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

Li, J., Pilato, G., Vassallo, G., Clements, M., Gentile, A., Sorbello, F., et al. (2006). Application of EalphaNets to Feature Recognition of Articulation Manner in Knowledge-Based Automatic Speech Recognition. In M.M. Bruno Apolloni (a cura di), Lecture Notes in Computer Science (pp. 140-146). Springer Verlag [10.1007/11731177_21].

Application of EalphaNets to Feature Recognition of Articulation Manner in Knowledge-Based Automatic Speech Recognition

VASSALLO, Giorgio;GENTILE, Antonio;SORBELLO, Filippo;SINISCALCHI, Sabato Marco
2006

Abstract

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.
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Li, J., Pilato, G., Vassallo, G., Clements, M., Gentile, A., Sorbello, F., et al. (2006). Application of EalphaNets to Feature Recognition of Articulation Manner in Knowledge-Based Automatic Speech Recognition. In M.M. Bruno Apolloni (a cura di), Lecture Notes in Computer Science (pp. 140-146). Springer Verlag [10.1007/11731177_21].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/59647
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