Automatic speech recognition (ASR) is increasingly used in daily applications, such as voice-activated virtual assistants like Siri and Alexa, real-time transcription for meetings and lectures, and voice commands for smart home devices. However, studies show that even state-of-the-art (SotA) ASR systems do not recognize the speech of everyone equally well.

To the best of my knowledge, this paper, for the first time, evaluates the performance of Meta's SotA ASR system, Massively Multilingual Speech (MMS), on Dutch native and non-native speech. Using the Jasmin Corpus dataset, which includes a diverse set of both native and non-native Dutch speakers, this study uses metrics such as word error rate (WER), character error rate (CER), and word information lost (WIL) to assess performance. Additionally, the same methodology is applied to the same data using OpenAI's ASR system, Whisper, to provide a comparative analysis.

The paper analyzes WER, CER, and WIL error metrics, processing time, and investigates the best-suited beam size for Whisper. It also lists out the types of errors made in terms of deletions, insertions, and substitutions made by each model across different age groups of Dutch speakers.

With the rise in the number of human-computer interactions, the need for systems that can accurately infer and respond to users' emotions becomes increasingly important. One can achieve this by examining audio-visual signals, aiming to identify the underlying emotions from an individual's gestures, auditory cues, and surroundings. Such automatic affect prediction systems depend heavily on labeled datasets. However, the subjective nature of emotion interpretation often introduces uncertainty, making it challenging to create reliable and high-quality datasets. To combat this issue, researchers have tried employing multiple raters to judge the affective state of a person, while employing interrater agreement measures to monitor uncertainty. To this moment, it is still unclear to what extend does reaching a good level of interrater agreement impact the performance of audio-visual Automatic Affect Prediction models.
As a first step towards understanding the potential influences on performance, this paper conducts a systematic literature review to investigate the objective annotation procedure in audio-visual databases. The survey extracted relevant literature from 4 scientific databases: Scopus, IEEE Xplore, Web of Science and ACM Digital Library. The results are aggregated from 55 papers and presented by following the PRISMA guidelines. They indicate that most databases use multiple annotators, a little more than half measure interrater agreement, and most train the raters to increase the uniformity of the labels.

Automatic Speech Recognition (ASR) technology is becoming more and more useful in everyday life, therefor also requiring higher accuracy across all different user demographics. This study compares the performance of Google's and Microsoft's ASR systems on native Dutch child and teenager speech using the JASMIN-CGN dataset as ASR for children presents unique challenges due to their shorter vocal tracts and irregular speech patterns. This research evaluates each system's performance based on Word Error Rate (WER) and Character Error Rate (CER), highlighting the differences between gender, age, and dialect regions. The results indicate that while Microsoft's ASR consistently outperforms Google's in terms of WER, Google demonstrates slightly higher precision in terms of CER. Therefor Microsoft is considered the better overall performing system but depending on one's needs, such as precision, Google would be the more favorable one.

Automatic Speech Recognition (ASR) systems are found in many places and are used by many people. Some groups of people, superficially older Dutch adults, are recognized less well by these systems. Given the aging population of the Netherlands, it would be beneficial to have ASR systems be more inclusive to allow for more independence of the older adults. By conducting tests on the ASR systems of Google and Microsoft, making use of the JASMIN dataset, I compared the two using word-error-rate (WER), word-information-lost (WIL) and character-error-rate (CER). Results show Microsoft outperforming Google with an average word error rate of 19.6\% compared to 27.35\%. However, Google is less biased on the topics of gender and age. Microsoft was slightly less biased in regards to region, but only by a small margin. Overall, the most notable findings from both systems are a small bias toward female speakers, and a strong bias against speakers from the southern regions of the Netherlands. These findings highlight the need for more inclusive ASR systems, enhancing the independence of older adults.

This paper compares the performance of two phonetic notations, IPA and ASJPcode, with the alphabetical notation for word-level language identification. Two machine learning models, a Multilayer Percerptron and a Logistic Regression model, are used to classify words using each of the three notations. With both models the IPA notation outperforms the other two notations.

Rhyming words are one of the most important features in poems. They add rhythm to a poem, and poets use this literary device to portray emotion and meaning to their readers. Thus, detecting rhyming words will aid in adding emotions and enhancing readability when generating poems. Previous studies have been done on the topic of poem generation. However, those works did not put too much emphasis on the rhyme detector. Thus, this research will solely focus on rhyme detection and its evaluation. The aim of this research is to determine the most accurate way of detecting whether two English words rhyme. English rhyming words will be detected using combinations of features. Five features are used: edit distance, hamming distance, jaccard similarity, longest common substring, and vowel and consonant weights. We also experiment with two methods of retrieving phonemes: using the entire phoneme translation, and using part of the phoneme translation. We find that using only hamming distance and jaccard similarity with part of the phoneme translation, we can already obtain an accuracy of 90.05% with a log loss of 0.25 when trained on a balanced dataset. The reason for this remains unclear because there is no clear separation between the two classes.