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Relevant bibliographies by topics / Viterbi smoothing

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Academic literature on the topic 'Viterbi smoothing'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Viterbi smoothing"

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Ghimire, Abhishek, and Rabin Dhakal. "HMM-Based POS Tagging in Hindi: A Viterbi Algorithm and Smoothing Analysis." European Journal of Applied Science, Engineering and Technology 3, no. 3 (2025): 372–82. https://doi.org/10.59324/ejaset.2025.3(3).26.

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Part-of-speech (POS) tagging plays a pivotal role in Natural Language Processing, providing valuable metadata for various downstream tasks such as syntactic parsing, information retrieval, sentiment analysis, machine translation, named entity recognition, and speech recognition. While significant progress has been made in POS tagging for English, the landscape for Hindi remains relatively underexplored. Limited availability of tagged datasets poses a unique challenge in training accurate POS taggers for Hindi. In this study, we aim to explore this gap by training a Hidden Markov Model (HMM) an

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NEY, H., V. STEINBISS, R. HAEB-UMBACH, B. H. TRAN, and U. ESSEN. "AN OVERVIEW OF THE PHILIPS RESEARCH SYSTEM FOR LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (1994): 33–70. http://dx.doi.org/10.1142/s0218001494000036.

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This paper gives an overview of a research system for phoneme based, large vocabulary continuous speech recognition. The system to be described has been applied to the SPICOS task, the DARPA RM task and a 12000 word dictation task. Experimental results for these three tasks will be presented. Like many other systems, the recognition architecture is based on an integrated statistical approach. In this paper, we describe the characteristic features of the system as opposed to other systems: (1) The Viterbi criterion is consistently applied both in training and testing. (2) Continuous mixture den

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3

Och, Franz Josef, and Hermann Ney. "A Systematic Comparison of Various Statistical Alignment Models." Computational Linguistics 29, no. 1 (2003): 19–51. http://dx.doi.org/10.1162/089120103321337421.

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We present and compare various methods for computing word alignments using statistical or heuristic models. We consider the five alignment models presented in Brown, Della Pietra, Della Pietra, and Mercer (1993), the hidden Markov alignment model, smoothing techniques, and refinements. These statistical models are compared with two heuristic models based on the Dice coefficient. We present different methods for combining word alignments to perform a symmetrization of directed statistical alignment models. As evaluation criterion, we use the quality of the resulting Viterbi alignment compared t

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Bello, Luca, John Wiedenhöft, and Alexander Schliep. "Compressed computations using wavelets for hidden Markov models with continuous observations." PLOS ONE 18, no. 6 (2023): e0286074. http://dx.doi.org/10.1371/journal.pone.0286074.

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Compression as an accelerant of computation is increasingly recognized as an important component in engineering fast real-world machine learning methods for big data; c.f., its impact on genome-scale approximate string matching. Previous work showed that compression can accelerate algorithms for Hidden Markov Models (HMM) with discrete observations, both for the classical frequentist HMM algorithms—Forward Filtering, Backward Smoothing and Viterbi—and Gibbs sampling for Bayesian HMM. For Bayesian HMM with continuous-valued observations, compression was shown to greatly accelerate computations

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Dissertations / Theses on the topic "Viterbi smoothing"

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Wu, Lan. "Multi-view hockey tracking with trajectory smoothing and camera selection." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2402.

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We address the problem of multi-view multi-target tracking using multiple stationary cameras in the application of hockey tracking and test the approach with data from two cameras. The system is based on the previous work by Okuma et al. [50]. We replace AdaBoost detection with blob detection in both image coordinate systems after background subtraction. The sets of blob-detection results are then mapped to the rink coordinate system using a homography transformation. These observations are further merged into the final detection result which will be incorporated into the particle filter. In a

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2

Huber, Stefan. "Voice Conversion by modelling and transformation of extended voice characteristics." Electronic Thesis or Diss., Paris 6, 2015. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2015PA066750.pdf.

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La Conversion de la Voix (VC) vise à transformer les caractéristiques de la voix d’un locuteur source de manière qu’il sera perçu comme étant prononcé par un locuteur cible. Le principe de la VC est de définir des fonctions du transposition pour la conversion de la voix de l’un locuteur source à la voix de l’un locuteur cible. Les fonctions de transformation de VC systèmes "State-Of-The-Art" (START) adapte instantanément aux caractéristiques de la voix source. Cependant, la qualité est pas encore suffisant. Des améliorations considérables sont nécessaires que les techniques VC peuvent être uti

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