Proposes an automatic training mechanism for phoneme recognition using labelless speech data under the condition that only its orthographical phonemic symbol sequence is given. For the purpose of obtaining better recognition performance the authors attempt to realize an automatic labeling procedure based on a phoneme classification method by mutual information criterion. By iterative training of a phoneme dictionary for a large amount of speech data, one can investigate the performance and convergence properties of the dictionary. From experimental results, the percent correct of the labeling is over 98% after three iterations, and for the phoneme recognition, a very high accuracy is also obtained.
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Publication status||Published - 1994|
|Event||Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust|
Duration: 1994 Apr 19 → 1994 Apr 22
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering