@inproceedings {762,
	title = {A multimodal variational approach to learning and inference in switching state space models},
	booktitle = {37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	year = {2004},
	address = {Montreal},
	abstract = {<p>An important general model for discrete-time signal processing is the switching state space (SSS) model, which generalizes the hidden Markov model and the Gaussian state space model. Inference and parameter estimation in this model are known to be computationally intractable. This paper presents a powerful new approximation to the SSS model. The approximation is based on a variational technique that preserves the multimodal nature of the continuous state posterior distribution. Furthermore, by incorporating a windowing technique, the resulting EM algorithm has complexity that is just linear in the length of the time series. An alternative Viterbi decoding with frame-based likelihood is also presented which is crucial for the speech application that originally motivates this work. Our experiments focus on demonstrating the effectiveness of the algorithm by extensive simulations. A typical example in speech processing is also included to show the potential of this approach for practical applications.</p>
},
	keywords = {continuous state posterior distribution, discrete time systems, discrete-time signal processing, EM algorithm, frame based representation, frame-based likelihood Viterbi decoding, Gaussian processes, Gaussian state space model, Hidden Markov Model, hidden Markov models, inference, learning, learning (artificial intelligence), model-based reasoning, multimodal variational technique, parameter estimation, speech processing, SSS model approximation, state-space methods, switching state space models, time series, variational techniques, Viterbi decoding, windowing technique},
	doi = {http://dx.doi.org/10.1109/ICASSP.2004.1327158},
	author = {L J. Lee and H Attias and L Deng and P Fieguth}
}
@inproceedings {529,
	title = {A functional articulatory dynamic model for speech production},
	booktitle = {27th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	year = {2001},
	address = {Utah},
	abstract = {<p>Introduces a statistical speech production model. The model synthesizes natural speech by modeling some key dynamic properties of vocal articulators in a linear/nonlinear state-space framework. The goal-oriented movements of the articulators (tongue tip, tongue dorsum, upper lip, lower lip, and jaw) are described in a linear dynamic state equation. The resulting articulatory trajectories, combined with the effects of the velum and larynx, are nonlinearly mapped into the acoustic feature space (MFCCs). The key challenges in this model are the development of a nonlinear parameter estimation methodology, and the incorporation of appropriate prior assumptions to assert in the articulatory dynamic structure. Such a model can also be directly applied to speech recognition to better account for coarticulation and phonetic reduction phenomena with considerably fewer parameters than HMM based approaches</p>
},
	keywords = {acoustic feature space, articulatory trajectories, coarticulation, dynamic properties, functional articulatory dynamic model, jaw, larynx, lower lip, matrix algebra, maximum likelihood estimation, natural speech, nonlinear parameter estimation methodology, parameter estimation, phonetic reduction phenomena, speech recognition, speech synthesis, state-space framework, state-space methods, statistical speech production model, tongue dorsum, tongue tip, upper lip, velum, vocal articulators},
	doi = {http://dx.doi.org/10.1109/ICASSP.2001.941035},
	author = {L J. Lee and P Fieguth and L Deng}
}
