Dysarthria is a speech disorder that limits an individual’s ability to clearly articulate, due to the weakening of the muscles involved in speech. Despite recent advances in Automatic Speech Recognition (ASR), the recognition of dysarthric speech remains a significant challenge because of the limited availability of dysarthric speech data, significant speaker variability, and the mismatch between typical and dysarthric speech patterns. This paper addresses these challenges by using transfer learning and Low-Rank Adaptation (LoRA) techniques to enhance the performance of the state- of-the-art ASR model Whisper on dysarthric speech. By fine-tuning Whisper with the TORGO dataset, this study aims to adapt the pre-trained models to better recognise dysarthric speech patterns, thus reducing Word Error Rates (WER) and improving accessibility for individuals with speech impairments. Experimental results indicate that this approach can improve speech recognition performance since the Large- V2, Large-V3 and the corresponding distilled models achieved a reduction in WER after fine-tuning. The Large-V3 model achieved the greatest relative WER reduction of 22.65%.

Automatic Speech Recognition (ASR) systems have transformed human-machine interaction, yet they often struggle with child speech due to the unique vocal characteristics. This thesis investigates age and gender biases, focusing on enhancing the performance of state-of-the-art ASR model Whisper on child speech. Initial experiments reveal significant disparities in recognition accuracy across age groups and genders within child speech, highlighting the critical need for targeted improvements. The study uses Low-Rank Adaptation (LoRA) to finetune the model using four child-specific datasets, aiming to simultaneously enhance recognition performance and mitigate biases. Results demonstrate substantial reductions in Word Error Rates (WER) and biases after finetuning, showcasing the effectiveness of transfer learning in addressing demographic inequality. Gender biases decreased by 32.77% relative to their initial values, and age biases also improved, with a relative decrease of 27.52% after finetuning. This research showcases the potential of tailored approaches to advance ASR technology for low-resource user demographics, with implications for improving educational and assistive technologies.

Index Terms: Automatic Speech Recognition, Child speech, Whisper ASR model, Age and gender biases, Low-Rank Adaptation, Transfer learning, Demographic disparities

This paper investigates how OpenAI’s Whisper model processes dysarthric speech by probing its internal acoustic feature representations. Utilizing the TORGO database, we analyzed Whisper’s capability to encode significant acoustic features specific to dysarthric speech across its encoding layers. Our findings reveal that initial layers are particularly effective in capturing distinct features, while deeper layers show generalized representations. Despite this, Whisper’s zero-shot performance in distinguishing dysarthric speech severity levels is noteworthy. We employed a series of probing tasks tailored to dysarthric speech characteristics to pinpoint specific features and their transformation across the model’s layers. This study highlights Whisper’s potential in handling atypical speech patterns without fine-tuning, paving the way for further research into the interpretability and application of transformer-based models in medical and assistive technologies. We discuss the implications of these results for enhancing transparency, reliability, and safe AI integration in healthcare.

Dysarthria is a speech disorder commonly caused by neurological disorders such as strokes, cerebral palsy and Amyotrophic Lateral Sclerosis (ALS). The severity level of dysarthria greatly influences the appropriate treatment for a patient. However, assessing the severity of dysarthria in a patient is a time-consuming process that requires a trained speech therapist. Therefore the following work explores a variety of classifier architectures for automatic dysarthria severity assessment using Whisper encodings. The datasets used were MSDM and TORGO while the classifier architectures implemented included a Convolutional Neural Networks and Recurrent Neural Network variants. Across both datasets, the Gated Recurrent Unit network (GRU) achieved the best performance with 97.21% accuracy on MSDM and 97.47% on TORGO.

Dysarthria is a motor speech disorder resulting in slurred or slow speech that can be difficult to understand. This re- search paper evaluates the effectiveness of various metrics for automatic speech recognition (ASR), such as character error rate (CER), Jaro-Winkler distance, and BERTscore, in assessing performance specifically for dysarthric speech, which is often inadequately measured by the commonly used word error rate (WER). Using the TORGO database, which includes a range of dysarthria severities, we analyze the performance of chosen evaluation metrics with the Whisper and wav2vec 2.0 ASR systems to understand how they reflect the true speech recognition challenges presented by such atypical speech pat- terns. Our findings reveal that Whisper generally outperforms wav2vec 2.0, particularly in sentence utterances, by effectively managing complex speech patterns and maintaining semantic integrity. The analysis highlights that single-word utterances strongly correlate with dysarthria severity, while sentence utterances show a lesser correlation due to the mitigating effect of additional linguistic context.

Automatic Speech Recognition (ASR) systems have become increasingly important for society, yet their performance varies significantly across different diverse speaker groups. With a significant non-native population in the Netherlands, it is crucial that ASR systems accurately recognize diverse speech. Commercial state-of-the-art ASR systems are yet under-explored in their performance on Dutch diverse speech. This study evaluates the performance of two recently developed and affordable ASR systems, Google Chirp and OpenAI's Whisper, on speech from native and non-native Dutch teenagers. This research evaluates the recognition accuracy of these ASR systems and identifies common transcription errors. The results show slightly worse performance compared to previous research on non-native speech, and Whisper performing generally better than Google Chirp on the speaker groups.

Automatic Speech Recognition is a field that has seen a strong increase in developments in recent years. In order to ensure objectivity and reliability in these systems, it is crucial they remain unbiased and treat speakers equally. This paper explores the bias of two state-of-the-art ASR systems in the domain of Dutch and Flemish speech, specifically towards regional dialects. Specifically, it explores Microsoft's Azure AI Speech Services ASR system and Google Chirp. It analyses the performance of these two systems on the JASMIN-CGN language corpus. The results show that speech from West-Dutch regions is recognized correctly significantly more often than other Dutch regions and speech from Brabant is recognized correctly significantly more often than other Flemish regions.

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) 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.

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.