Evaluating the Use of Pitch Shifting to Improve Automatic Speech Recognition Performance on Southern Dutch Accents

Building Automatic Speech Recognizers (ASRs) has been a challenge in languages with insufficiently sized corpora or data sets. A further large issue in language corpora is biases against regionally accented speech and other speaker attributes. There are some techniques to improve ASR performance and reduce biases in these corpora, known as data...

A cellular automaton for modelling territories

A cellular automaton for simulating territories is presented. In it, cells have a certain amount of markings of two different groups. The amount of markings for each group gets higher based on the amounts of that group in neighboring cells and the amount of markings of the opposite group in the current cell to simulate an avoidance tendency. The...

Implementation and Evaluation of an Order Parameter for the Reaction-Diffusion Model in a Cellular Automaton: How does an order parameter perform on a Reaction-Diffusion model implemented in a Cellular Automata?

This paper describes the process and evaluation methods by which we adapted a Reaction-Diffusion model and an order parameter to monitor its segregation state in a 2D Cellular Automaton model. The model simulates Turing pattern formations, whose behavior will be studied by an order parameter formulated for this project. This order parameter acts...

Multi species turing patterns in three dimensions

In 1952, Alan M. Turing presented a reaction-diffusion model that described formation of skin patterns. The patterns he predicted have later been found in various natural phenomena, such as in skins of fish or even in vegetation around termite hills. His patterns have even been taken to the micro-level. In 1984, David A. Young proposed a...

Explainable Artificial Intelligence (XAI) Techniques - A Review and Case Study

The significant progress of Artificial Intelligence (AI) and Machine Learning (ML) techniques such as Deep Learning (DL) has seen success in their adoption in resolving a variety of problems. However, this success has been accompanied by increasing model complexity resulting in a lack of transparency and trustworthiness. Explainable Artificial...

Investigation and Comparison of Evaluation Methods of Model-Agnostic Explainable AI Models

Many artificial intelligence (AI) systems are built using black-box machine learning (ML) algorithms. The lack of transparency and interpretability reduces their trustworthiness. In recent years, research into explainable AI (XAI) has increased. These systems are designed to tackle common ML issues such as trust, accountability, and transparency...

Improving ASR performance on Jasmin Flemish Dutch data by performing frequency perturbation

ASR (automatic speech recognition) systems are used widely in our current day and age. However, for a technology that is used so much in our daily life it contains a lot of bias. This means that not all people can use it equally, people with a different gender, age and dialect will all see different results. The goal of this paper is to reduce...

Improving Northern Regional Dutch Speech Recognition by Adapting Perturbation-based Data Augmentation

Automatic speech recognition (ASR) does not perform equally well on every speaker. There is bias against many attributes, including accent. To train Dutch ASR, there exists CGN(Corpus Gesproken Nederlands) and as an extension, the JASMIN corpus with annotated accented data. This paper focuses on improving ASR performance for NRAD (Northern...

Evaluating the Use of Frequency Masking on a Hybrid Automatic Speech Recognizer for Transitional Dutch Accent of JASMIN-CGN Corpus

There are many experiments conducted with Automatic Speech Recognition (ASR) systems, but many either focus on specific speaker categories or on a language in general. Therefore, bias could occur in such ASR systems towards different genders, age groups, or dialects. But, to analyze and reduce bias, the models require significant amounts of data...

Model-Agnostic XAI Models: Benefits, Limitations and Research Directions

The ever increasing presence of Machine Learning (ML) algorithms and Artificial Intelligence (AI) agents in safety-critical and sensitive fields over the past few years has spurred massive amounts of research in Explainable Artificial Intelligence (XAI) techniques (models). This new frontier of AI research aims to resolve some of the fundamental...

Investigation of the evaluation techniques and tools used for model-specific XAI models

The spread of AI techniques has lead to its presence in critical situations, with increasing performance that can compromise on its understanding. Users with no prior AI knowledge rely on these techniques such as doctors or recruiters with a need for transparency and comprehensibility of the mechanisms. The advent of Explainable Artificial...

Evaluating the Effect of SpecSwap for Purposes of Improving WER Performance of the Western Dutch Region Using the JASMIN-CGN Dataset

A problem prevalent in many modern-day Automatic Speech Recognition (ASR) systems is the presence of bias and its reduction. Bias can be observed when an ASR system performs worse on a subset of its speakers compared to the rest rather than having the same overall generalization for everyone. This can be seen by using Word Error Rates (WER) as a...

ELM Decision Branching: Improving streaming clustering with decision branching

Clustering is a commonly used method in data analysis. It is a complex problem that can be very time consuming, especially when clustering large datasets with many features. Most clustering algorithms scale exponentially in time when increasing the dataset size, making it infeasible to use them for large datasets. Streaming algorithms do not...

Spatial Profiling of Tumor Microenvironments in Breast Cancer

The tumor composition of breast cancer determines how tumors behave. Yet, there is a limited understanding of the arrangement of tumor cells in relation to cells in the tumor microenvironment (TME). In this research, we have characterized distance relationships between 324 cell-type pairs in 749 tissue samples of the Molecular Taxonomy of Breast...

Exploring deep learning to improve allelic peak calling in forensic DNA analysis

When processing a trace DNA sample at the Netherlands Forensic Institute, an STR electropherogram can be created. An analyst uses this electropherogram and analysis software to read out peaks signifying DNA. After analysis, the DNA profile is used in the interpretation process, which can include the comparison to a reference DNA profile of a...

Embedded Neural Networks for Continuous Patient Posture Classification

Current hospital protocols dictate patients be turned at least every three hours in the effort of preventing pressure ulcers. To reduce the workload of nurses, Momo Medical has created an embedded sensing device to track the patient's posture and notify nurses when it is time to turn them. The challenge presented and the focus of this thesis is...

Accelerating DNA basecalling of Nanopore reads on FPGAs

Genomics has revolutionized our understanding of evolution, hereditary diseases, and more. The advent of long-read DNA sequencers i.e. Oxford Nanopore Technologies' innovations, has opened many new research potentials in genomics. These sequencers produce significantly longer DNA reads, facilitating novel applications. However, this...