Title :
Generalized minimal distortion segmentation for ANN-based speech recognition
Author :
Chen, Sin-Horng ; Chen, Wen-Yuan
Author_Institution :
Dept. of Commun. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fDate :
3/1/1995 12:00:00 AM
Abstract :
A generalized minimal distortion segmentation algorithm is proposed to solve the time alignment problem for ANN-based speech recognition. By modeling dynamics of spectral information of an acoustic segment with smooth curves obtained by orthonormal polynomial expansion, a speech signal is optimally divided into segments and then recognized by an MLP recognizer. Experimental results showed that the proposed method outperforms the standard CDHMM method
Keywords :
neural nets; polynomials; spectral analysis; speech recognition; ANN-based speech recognition; MLP recognizer; acoustic segment; generalized minimal distortion segmentation; orthonormal polynomial expansion; spectral information; speech signal; time alignment problem; Acoustic distortion; Artificial neural networks; Data preprocessing; Hidden Markov models; Information processing; Neural networks; Polynomials; Signal processing; Speech processing; Speech recognition;
Journal_Title :
Speech and Audio Processing, IEEE Transactions on
