A comparison of trajectory and mixture modeling in segment-based word recognition

Kannan, A.; Ostendorf, Mari

doi:10.1109/icassp.1993.319303

Cited by 11 publications

(10 citation statements)

References 3 publications

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“…It is worth pointing out another work (Kannan & Ostendorf, 1993) which also models trajectories and uses mixture distributions. The difference is that we define mixtures on the trajectory level, rather than on the state (micro-segment) level (Kannan & Ostendorf, 1993), and in fact we are using a form of context dependence. The context dependence here is data driven, and obtained via clustering (as we use a mixture of models per phoneme), as opposed to being linguistically motivated.…”

Section: Resultsmentioning

confidence: 99%

Estimation of mixtures of stochastic dynamic trajectories: application to continuous speech recognition

Afify

Gong

Haton

1996

Computer Speech & Language

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Section: Resultsmentioning

confidence: 99%

Estimation of mixtures of stochastic dynamic trajectories: application to continuous speech recognition

Afify

Gong

Haton

1996

Computer Speech & Language

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“…The generalization from the the single-trend model can be viewed as providing discrete-mode distributions on the segment-bound polynomial parameters. 10 Development of this new model is motivated mainly by the observation that contextual and speaker variations bring about widely varying trajectory shapes of the acoustic data in fluent, speakerindependent speech examined in the TIMIT data base. The speech recognition evaluation results we have obtained so far show consistent performance improvement in the recognizer based on the new model.…”

Section: Summary and Discussionmentioning

confidence: 99%

Speaker-independent phonetic classification using hidden Markov models with mixtures of trend functions

Deng

Aksmanovic

1997

IEEE Trans. Speech Audio Process.

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In this study, we make a major extension of the nonstationary-state or trended hidden Markov model (HMM) from the previous single-trend formulation [2], [3] to the current mixture-trended one. This extension is motivated by the observation of wide variations in the trajectories of the acoustic data in fluent, speaker-independent speech associated with a fixed underlying linguistic unit. It is also motivated by potential use of mixtures of trend functions to characterize heterogeneous time-varying data generated from distinctive sources such as the speech signals collected from different microphones or from different telephone channels. We show how HMM's with mixtures of trend functions can be implemented simply in the already well-established single-trend HMM framework via the device of expanding each state into a set of parallel states. Details of a maximum-likelihood-based (ML-based) algorithm are given for estimating state-dependent mixture trajectory parameters in the model. Experimental results on the task of classifying speaker-independent vowels excised from the TIMIT data base demonstrate consistent performance improvement using phonemic mixture-trended HMM's over their single-trend counterpart.

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“…Continuous explicit variable duration HMM is adopted in the speech recognition. Compared with standard HMM, results show that the absence of a correct duration model increases the error rate by 50% [4][5][6] . Due to the inherent ambiguity related to the segmentation process in handwritten words, it is a practical idea to use the variable duration model for the states in an HMM based handwritten word recognition (HWR) system [7,8] .…”

Section: Introductionmentioning

confidence: 92%

A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM)

Dong

2008

Sci. China Ser. F-Inf. Sci.

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As a new maintenance method, CBM (condition based maintenance) is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and practice in industry is effective diagnostics and prognostics. Recently, a pattern recognition technique called HMM (hidden Markov model) was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM). Compared with HMM, AR-HSMM has three advantages: 1) It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2) It can relax observations' independence assumption by accommodating a link between consecutive observations. 3) It does not follow the unrealistic Markov chain's memoryless assumption and therefore provides more powerful modeling and analysis capability for real problems. To facilitate the computation in the proposed AR-HSMM-based diagnostics and prognostics, new forwardbackward variables are defined and a modified forward-backward algorithm is developed. The evaluation of the proposed methodology was carried out through a real world application case study: health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decisionmaking in equipment health management.auto-regressive hidden semi-Markov model, diagnosis, prognosis, Markov model

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A comparison of trajectory and mixture modeling in segment-based word recognition

Abstract: a discussion of our results and possible future work. This paper presents a mechanism for implementing 2. MICROSEGMENT FRAMEWORK mixtures at a phone-subsegment (microsegment) level The framework consists of two levels: the upper level

Cited by 11 publications

References 3 publications

Estimation of mixtures of stochastic dynamic trajectories: application to continuous speech recognition

Estimation of mixtures of stochastic dynamic trajectories: application to continuous speech recognition

Speaker-independent phonetic classification using hidden Markov models with mixtures of trend functions

A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM)

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