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Stochastic Modelling: From Pattern Classification to Speech Recognition and Language Translation
[chapter]
1999
Informatik aktuell
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Starting with the Bayes decision rule as in pattern classification and speech recognition, we show how the resulting system architecture can be structured into three parts: the language model probability ...
This paper gives an overview of the stochastic modelling approach to machine translation. ...
Acknowledgment This paper is based on work supported partly by the VERBMOBIL project (contract number 01 IV 701 T4) by the German Federal Ministry of Education, Science, Research and Technology and as ...
doi:10.1007/978-3-642-60243-6_38
dblp:conf/dagm/Ney99
fatcat:axrqx5shkneknivxvragf56aya
Stochastic Game Model of Data Clustering
2021
International Workshop on Intelligent Information Technologies & Systems of Information Security
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A stochastic game model of data clustering under interference conditions is proposed. An adaptive recurrent method and algorithm for stochastic game deciding have developed. ...
For this purpose, each data point is considered as a separate player with the ability to learn and adapt to the uncertainties of the system. ...
Introduction Clustering can accomplished by solving data mining and data visualization problems, grouping and pattern recognition, knowledge extraction and information retrieval, and object classification ...
dblp:conf/intelitsis/KravetsBOVDL21
fatcat:fd4sddz3abdhbcw6xavcdcrlfq
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
[article]
2017
arXiv
pre-print
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Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it becomes harder to derive and implement those of a larger scale model. ...
Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. ...
Moreover,Ŝ is sent to the speech recognition model, and the parameter L of the language model is updated. ...
arXiv:1712.00929v3
fatcat:slwdgl5chjchrdb5qykrh7iqry
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
2018
Frontiers in Neurorobotics
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model and (B) end-to-end learning. ...
However, it has become harder to derive and implement equations of large-scale models. ...
(32) Moreover,Ŝ is sent to the speech recognition model, and the parameter L of the language model is updated. ...
doi:10.3389/fnbot.2018.00025
pmid:29997493
pmcid:PMC6028621
fatcat:c7jyjhm6t5b4rfahljkxhf5z2e
Objective Human Affective Vocal Expression Detection and Automatic Classification with Stochastic Models and Learning Systems
[article]
2019
arXiv
pre-print
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The α-integrated Gaussian model (α-GMM) is also introduced for the emotion representation and classification. Its performance is compared to competing stochastic and machine learning classifiers. ...
The proposed features are evaluated in speech emotion classification experiments with three databases in German and English languages. ...
It is compared to classic Gaussian Mixture Models (GMM) [24] and Hidden Markov Models (HMM) [25] stochastic methods, and also machine learning approaches: Support Vector Machines (SVM) [26] , Deep ...
arXiv:1910.01967v1
fatcat:arwvbtnurvakdfffcyhevvbclm
Hilbert–Huang–Hurst-based non-linear acoustic feature vector for emotion classification with stochastic models and learning systems
2020
IET Signal Processing
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The proposed feature vector is evaluated in speech emotion classification experiments with three databases in German and English languages. ...
Its performance is compared to competing for stochastic and machine learning classifiers. ...
Stochastic classifiers such as Gaussian mixture model (GMM) [24] and hidden Markov model (HMM) [25] were widely adopted for speaker and speech recognition tasks. ...
doi:10.1049/iet-spr.2019.0383
fatcat:6wpeyfpih5bvlnebkv4f6vxtwi
Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages
2011
IEEE Transactions on Audio, Speech, and Language Processing
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recognition (ASR) system. ...
Following a detailed description of the models, experimental and comparative results are presented on three corpora in different languages and with different complexity. ...
His research on stochastic models for speech and language processing has been applied to a wide range of speech and language tasks. ...
doi:10.1109/tasl.2010.2093520
fatcat:ibtf5zvxxncvldslohguwmn7tm
On the Dangers of Stochastic Parrots
2021
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
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language models. ...
The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. ...
Initially proposed by Shannon in 1949 [117] , some of the earliest implemented LMs date to the early 1980s and were used as components in systems for automatic speech recognition (ASR), machine translation ...
doi:10.1145/3442188.3445922
fatcat:qoqcd66fsnc4pdfhebn3mbq5ba
The Stochastic complexity of spin models: Are pairwise models really simple?
[article]
2018
Entropy
accepted
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In information theory and Bayesian inference, the simplicity of a model is precisely quantified in the stochastic complexity, which measures the number of bits needed to encode its parameters. ...
In order to understand how simple models look like, we study the stochastic complexity of spin models with interactions of arbitrary order. ...
Yet, problems such as image or speech recognition and language translation have shown that Big Data can solve problems without necessarily understanding them [1] [2] [3] . ...
doi:10.3390/e20100739
pmid:33265828
pmcid:PMC7512302
arXiv:1702.07549v3
fatcat:qowbvsomtzbkpl72lrgqfkt7km
Learning Stochastic Finite Automata for Musical Style Recognition
[chapter]
2006
Lecture Notes in Computer Science
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We use them to model musical styles: a same automaton can be used to classify new melodies but also to generate them. ...
Stochastic deterministic finite automata have been introduced and are used in a variety of settings. ...
Acknowledgement: The authors are grateful to Pedro Cruz for his benchmarks and for many ideas used in this work. They also thank Thierry Murgue and Franck Thollard for help with Mdi and parsers. ...
doi:10.1007/11605157_31
fatcat:k3m66q4ndjfshmcktmk7kougua
Stochastic Language Generation in Dialogue using Factored Language Models
2014
Computational Linguistics
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realisation phrases according to Factored Language Models (FLMs). ...
Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of pre-generated utterances, or (b) using statistics to determine the generation ...
Dynamic Bayesian networks have been used successfully for speech recognition, natural language understanding, dialogue management and text-to-speech synthesis (Rabiner 1989; He and Young 2005; Lefèvre ...
doi:10.1162/coli_a_00199
fatcat:ehegb4qj3bgm3hguv3av6cvtce
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders
2019
Proceedings of the 2019 Conference of the North
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In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). ...
We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. ...
All views expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation. ...
doi:10.18653/v1/n19-1007
dblp:conf/naacl/ShainE19
fatcat:dhkruz4wjnarrawba54tvwce74
Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training
2018
PLoS ONE
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This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. ...
A machine translation task shows that the proposed model constitutes a good solution to the normalization cost of the output softmax layer of Neural Networks, for some practical cases, without a significant ...
Acknowledgments Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R (to MJCB). ...
doi:10.1371/journal.pone.0200884
pmid:30048480
pmcid:PMC6062053
fatcat:tkck5lx3urefhp6lpcllae24d4
Some Statistical-Estimation Methods for Stochastic Finite-State Transducers
2001
Machine Learning
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Formal translations constitute a suitable framework for dealing with many problems in pattern recognition and computational linguistics. ...
The application of formal transducers to these areas requires a stochastic extension for dealing with noisy, distorted patterns with high variability. ...
Acknowledgment The authors wish to thank the three anonymous reviewers for their criticisms and suggestions. ...
doi:10.1023/a:1010880113956
dblp:journals/ml/PicoC01
fatcat:ldd7wyx5x5h7je5gjjizzhlapq
Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder
2022
International Journal of Power Electronics and Drive Systems (IJPEDS)
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According to the standard body, there are classification of discrete forms of bipolar disorder viz. type-I, type-II, and cyclothymic. ...
Hence, the model contributes to obtain granular information with dynamics of mood transition. ...
A discriminant classification over linear model of regression is designed to carry out this form of detection. Apart from detection-based approach, there is also recognition-based approach. ...
doi:10.11591/ijece.v12i1.pp620-629
fatcat:6dfxtexolbevvlvnma5hbogjsi
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