Automatic segmentation and labeling of speech based on Hidden Markov Models
Speech Communication
A hierarchical method of automatic speech segmentation for synthesis applications
Speech Communication
Automatic segmentation of speech recorded in unknown noisy channel characteristics
Speech Communication - Special issue on robust speech recognition
An Introduction to Text-to-Speech Synthesis
An Introduction to Text-to-Speech Synthesis
Training v-support vector regression: theory and algorithms
Neural Computation
Phonetic alignment: speech synthesis-based vs. viterbi-based
Speech Communication
Neural Computation
Multi-lingual label alignment using acoustic-phonetic features derived by neural-network technique
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Automatic segmentation and labeling of speech
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Simultaneous speech segmentation and phoneme recognition using dynamic programming
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Automatic segmentation of speech
IEEE Transactions on Signal Processing
Automatic Phonetic Segmentation by Score Predictive Model for the Corpora of Mandarin Singing Voices
IEEE Transactions on Audio, Speech, and Language Processing
A Large Margin Algorithm for Speech-to-Phoneme and Music-to-Score Alignment
IEEE Transactions on Audio, Speech, and Language Processing
On Using Multiple Models for Automatic Speech Segmentation
IEEE Transactions on Audio, Speech, and Language Processing
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Adaptive phoneme alignment based on rough set theory
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Improvements on automatic speech segmentation at the phonetic level
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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In the present work we study the appropriateness of a number of linear and non-linear regression methods, employed on the task of speech segmentation, for combining multiple phonetic boundary predictions which are obtained through various segmentation engines. The proposed fusion schemes are independent of the implementation of the individual segmentation engines as well as from their number. In order to illustrate the practical significance of the proposed approach, we employ 112 speech segmentation engines based on hidden Markov models (HMMs), which differ in the setup of the HMMs and in the speech parameterization techniques they employ. Specifically we relied on sixteen different HMMs setups and on seven speech parameterization techniques, four of which are recent and their performance on the speech segmentation task have not been evaluated yet. In the evaluation experiments we contrast the performance of the proposed fusion schemes for phonetic boundary predictions against some recently reported methods. Throughout this comparison, on the established for the phonetic segmentation task TIMIT database, we demonstrate that the support vector regression scheme is capable of achieving more accurate predictions, when compared to other fusion schemes reported so far.