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 augmentations. One audio data augmentation, pitch shifting, has had successes in other experiments for increasing ASR performance. Pitch shifting it is tested in this paper on the JASMIN-CGN speech data set from the Southern regions of the Netherlands. Using a hybrid GMM-HMM ASR, two baselines are developed, one using all speech data from the region, the other only using native speech. For the former ASR, pitch shifting is found to not improve Word Error Rate (WER) performance or reduce bias, but the latter succeeds in improving WER performance and reduced bias for certain speaker groups when augmented.

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 regional accented Dutch) speech, training on speakers from the region of Overijssel. To achieve this improvement, the corpus data is augmented using Vocal Tract Length Perturbation (VTLP), which entails randomly warping the frequency of each recording using a factor in the range [0.9, 1.1]. The baseline and augmented ASR systems are trained using trigram GMM-HMM (Gaussian mixture model hidden Markov models) through the Kaldi toolkit on the DelftBlue supercomputer. This leads to improvements on word error rates (WER) for all speaker groups and styles, with an overall relative improvement of 14,64% and the biggest improvement observed for male speakers - from 25.15% WER to 19,68% WER. The impact of this augmentation on other accents and non-accented speech is not explored. This experiment can serve as a stepping stone for developing overall more robust and less biased Dutch ASR.

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 metric. Depending on the ASR system in question the type of bias differs. However, techniques have been proposed and shown to succeed in reducing WER, and subsequently bias, by the use of data augmentation techniques for the recorded speech. These techniques perturb the audio in a certain way. Afterward, it is added to a model's training set and the model is retrained with the added data. One such technique is SpecSwap. This paper explores how using SpecSwap affects the WER performance of a hybrid-model ASR system using the JASMIN-CGN dataset's West-Dutch region. For comparison, a state-of-the-art data augmentation technique, VTLP, was also used, which has been shown to be effective in other cases. The experiments both led to a consistent WER increase. Therefore it was concluded that the data provided for the region was too little for the augmentation policy to be effective in any of the subcategories or in the overall performance of the system. However, SpecSwap shows potential in mitigating the widely discussed gender bias in ASR systems by reducing the difference between male and female speakers' WER.

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 this bias, in this case the dialect Flemish Dutch by increasing the performance of this dialect. Since collecting data is expensive, a data augmentation technique has been used. This technique has been used to increase the training data and lower the word error rate of this dialect. Frequency perturbation was used as the data augmentation technique. This technique amplifies or reduces the amplitude of certain frequency bands. We managed to improve upon the Flemish Dutch dialect slightly. Even though the dialect is still quite a bit worse compared to other Dutch dialects, it was improved nonetheless.

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 to be trained on, and some corpora lack that. This is where augmentation techniques can be used to generate more unique data without any further collection of it. This paper explores the use of SpecAugment's frequency masking on such a corpus, JASMIN-CGN, for the Transitional regional accent of Dutch, with a hybrid GMM-HMM architecture, in order to reduce the bias for gender or age, for this specific dialect. The experiments show that SpecAugment does not manage to lower the WER (20.8% overall compared to the baseline model, which achieves 19.5% performance), on the contrary, it even increases the bias for age. The results are mainly attributed to the combination of low amounts of data + the hybrid architecture used, which proves SpecAugment to be a useful augmentation policy only for end-to-end models.

One of the most important problems that needs tackling for wide deployment of Automatic Speech Recognition (ASR) is the bias in ASR, i.e., ASRs tend to generate more accurate predictions for certain speaker groups while making more errors on speech from others. In this thesis, we aim to reduce bias against non-native speakers of Dutch compared to native Dutch speakers. Typically, an important source of bias is insufficient training data. We therefore investigate employing three different data augmentation techniques to increase the amount of non-native accented Dutch training data, i.e., speed and volume perturbation and pitch shift, and using these for two transfer learning techniques: model fine-tuning and multi-task learning, to reduce bias in a state-of-the-art hybrid HMM-DNN Kaldi-based ASR system. Experimental results on read speech and human-computer interaction (HMI) speech showed that although individual data augmentation techniques did not always yield an improved recognition performance, the combination of all three data augmentation techniques did. Importantly, bias was reduced by more than 18% absolute compared to the baseline system for read speech when applying pitch shift data augmentation and multi-task training, and by more than 7% for HMI speech when applying all three data augmentation techniques during fine-tuning, while improving recognition accuracy of both the native and non-native Dutch speech.