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pro vyhledávání: '"Clemmensen, Line H."'
On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms
Speech enhancement techniques improve the quality or the intelligibility of an audio signal by removing unwanted noise. It is used as preprocessing in numerous applications such as speech recognition, hearing aids, broadcasting and telephony. The eva
Externí odkaz:
http://arxiv.org/abs/2306.01538
Use of speech models for automatic speech processing tasks can improve efficiency in the screening, analysis, diagnosis and treatment in medicine and psychiatry. However, the performance of pre-processing speech tasks like segmentation and diarizatio
Externí odkaz:
http://arxiv.org/abs/2204.11550
Autor:
Das, Sneha, Lund, Nicklas Leander, Lønfeldt, Nicole Nadine, Pagsberg, Anne Katrine, Clemmensen, Line H.
Speech emotion recognition~(SER) refers to the technique of inferring the emotional state of an individual from speech signals. SERs continue to garner interest due to their wide applicability. Although the domain is mainly founded on signal processi
Externí odkaz:
http://arxiv.org/abs/2203.14867
In recent years, speech emotion recognition (SER) has been used in wide ranging applications, from healthcare to the commercial sector. In addition to signal processing approaches, methods for SER now also use deep learning techniques which provide t
Externí odkaz:
http://arxiv.org/abs/2203.14865
Compressing CNN Kernels for Videos Using Tucker Decompositions: Towards Lightweight CNN Applications
Convolutional Neural Networks (CNN) are the state-of-the-art in the field of visual computing. However, a major problem with CNNs is the large number of floating point operations (FLOPs) required to perform convolutions for large inputs. When conside
Externí odkaz:
http://arxiv.org/abs/2203.07033
Data representativity is crucial when drawing inference from data through machine learning models. Scholars have increased focus on unraveling the bias and fairness in models, also in relation to inherent biases in the input data. However, limited wo
Externí odkaz:
http://arxiv.org/abs/2203.04706
In recent years, speech emotion recognition (SER) has been used in wide ranging applications, from healthcare to the commercial sector. In addition to signal processing approaches, methods for SER now also use deep learning techniques. However, gener
Externí odkaz:
http://arxiv.org/abs/2105.02055
The elastic-net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penalized maximum likelihood. Its nice properties originate from a combinatio
Externí odkaz:
http://arxiv.org/abs/2006.01671
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low vari
Externí odkaz:
http://arxiv.org/abs/1605.09196
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