Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Automatic segmentation of speech recorded in unknown noisy channel characteristics
Speech Communication - Special issue on robust speech recognition
DISTBIC: a speaker-based segmentation for audio data indexing
Speech Communication - Special issue on accessing information in spoken audio
On the complexity of additive clustering models
Journal of Mathematical Psychology
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
A Two-level Method for Unsupervised Speaker-based Audio Segmentation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Context-Dependent Boundary Model for Refining Boundaries Segmentation of TTS Units
IEICE - Transactions on Information and Systems
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Review: Speaker segmentation and clustering
Signal Processing
Robust detection of phone boundaries using model selection criteria with few observations
IEEE Transactions on Audio, Speech, and Language Processing
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In this work, we present a text-independent automatic phone segmentation algorithm based on the Bayesian Information Criterion. Speech segmentation at a phone level imposes high resolution requirements in the short-time analysis of the audio signal; otherwise the limited information available in such a small scale would be too restrictive for an efficient characterisation of the signal. In order to alleviate this problem and detect the phone boundaries accurately, we employ an information criterion corrected for small samples while modelling speech samples with the generalised Gamma distribution, which offers a more efficient parametric characterisation of speech in the frequency domain than the Gaussian distribution. Using a computationally inexpensive maximum likelihood approach for parameter estimation, we evaluate the efficiency of the proposed algorithm in M2VTS and NTIMIT data sets and we demonstrate that the proposed adjustments yield significant performance improvement in noisy environments.