Non-linear Predictive Models for Speech Processing

2005 ◽  
pp. 779-784
Author(s):  
M. Chetouani ◽  
Amir Hussain ◽  
M. Faundez-Zanuy ◽  
B. Gas
Keyword(s):  
Speech Processing ◽  
Predictive Models ◽  
Non Linear

Non-Linear Speech Processing (NOLISP 2013)

2015 ◽  
Vol 30 (1) ◽  
pp. 1-2
Author(s):  
Thomas Drugman
Keyword(s):  
Speech Processing ◽  
Non Linear

Special issue on non-linear and non-conventional speech processing

2009 ◽  
Vol 51 (9) ◽  
pp. 713
Author(s):  
Mohamed Chetouani ◽  
Marcos Faundez-Zanuy ◽  
Amir Hussain ◽  
Bruno Gas ◽  
Jean-Luc Zarader ◽  
...  
Keyword(s):  
Speech Processing ◽  
Special Issue ◽  
Non Linear

Development of predictive models for effective process parameter selection for single and overlapping laser clad bead geometry

2018 ◽  
Vol 24 (1) ◽  
pp. 214-228 ◽  
Author(s):  
Kush Aggarwal ◽  
R.J. Urbanic ◽  
Syed Mohammad Saqib
Keyword(s):  
Data Collection ◽  
Predictive Model ◽  
Laser Cladding ◽  
Process Planning ◽  
Process Parameters ◽  
Predictive Models ◽  
Goodness Of Fit ◽  
Bead Geometry ◽  
Content Type ◽  
Non Linear

Purpose The purpose of this work is to explore predictive model approaches for selecting laser cladding process settings for a desired bead geometry/overlap strategy. Complementing the modelling challenges is the development of a framework and methodologies to minimize data collection while maximizing the goodness of fit for the predictive models. This is essential for developing a foundation for metallic additive manufacturing process planning solutions. Design/methodology/approach Using the coaxial powder flow laser cladding method, 420 steel cladding powder is deposited on low carbon structural steel plates. A design of experiments (DOE) approach is taken using the response surface methodology (RSM) to establish the experimental configuration. The five process parameters such as laser power, travel speed, etc. are varied to explore their impact on the bead geometry. A total of three replicate experiments are performed and the collected data are assessed using a variety of methods to determine the process trends and the best modelling approaches. Findings There exist unpredictable, non-linear relationships between the process parameters and the bead geometry. The best fit for a predictive model is achieved with the artificial neural network (ANN) approach. Using the RSM, the experimental set is reduced by an order of magnitude; however, a model with R2 = 0.96 is generated with ANN. The predictive model goodness of fit for a single bead is similar to that for the overlapping bead geometry using ANN. Originality/value Developing a bead shape to process parameters model is challenging due to the non-linear coupling between the process parameters and the bead geometry and the number of parameters to be considered. The experimental design and modelling approaches presented in this work illustrate how designed experiments can minimize the data collection and produce a robust predictive model. The output of this work will provide a solid foundation for process planning operations.

scholarly journals Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

2020 ◽  
Vol 7 ◽  
Author(s):  
David Medina-Ortiz ◽  
Sebastián Contreras ◽  
Cristofer Quiroz ◽  
Álvaro Olivera-Nappa
Keyword(s):  
Supervised Learning ◽  
Predictive Models ◽  
Non Linear

scholarly journals Parallel processing in speech perception: Local and global representations of linguistic context

2021 ◽  
Author(s):  
Christian Brodbeck ◽  
Shohini Bhattasali ◽  
Aura A L Cruz Heredia ◽  
Philip Resnik ◽  
Jonathan Z Simon ◽  
...  
Keyword(s):  
Speech Processing ◽  
Predictive Models ◽  
Local Context ◽  
Linguistic Context ◽  
Neural Responses ◽  
Temporal Lobes ◽  
Sentence Level ◽  
Available Information ◽  
Context Models ◽  
Context Free

Speech processing is highly incremental. It is widely accepted that listeners continuously use the linguistic context to anticipate upcoming concepts, words and phonemes. However, previous evidence supports two seemingly contradictory models of how predictive cues are integrated with bottom-up evidence: Classic psycholinguistic paradigms suggest a two-stage model, in which acoustic input is represented fleetingly in a local, context-free manner, but quickly integrated with contextual constraints. This contrasts with the view that the brain constructs a single unified interpretation of the input, which fully integrates available information across representational hierarchies and predictively modulates even earliest sensory representations. To distinguish these hypotheses, we tested magnetoencephalography responses to continuous narrative speech for signatures of unified and local predictive models. Results provide evidence for some aspects of both. Local context models, one based on sublexical phoneme sequences, and one based on the phonemes in the current word alone, do uniquely predict some part of early neural responses; at the same time, even early responses to phonemes also reflect a unified model that incorporates sentence level constraints to predict upcoming phonemes. Neural source localization places the anatomical origins of the different predictive models in non-identical parts of the superior temporal lobes bilaterally, although the more local models tend to be right-lateralized. These results suggest that speech processing recruits both local and unified predictive models in parallel, reconciling previous disparate findings. Parallel models might make the perceptual system more robust, facilitate processing of unexpected inputs, and serve a function in language acquisition.

scholarly journals Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

2010 ◽  
Vol 2 (3) ◽  
pp. 133-134 ◽  
Author(s):  
Jordi Solé-Casals ◽  
Vladimir Zaiats ◽  
Enric Monte-Moreno
Keyword(s):  
Speech Processing ◽  
Non Linear ◽  
Alternative Techniques
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