Brain waves, speech, and music are all phenomena that can be encoded in digital signals. On the most basic level, a digital signal is a numerical representation of a stream of data over time. Signal processing can also include multidimensional data arrays, such as videos or fMRIs (functional magnetic resonance imaging). The real power of digital signals is that they allow for flexible manipulation of these data in order to change the output (voice changing, audio processing/effects) or analyze the signal (spectral analysis, measuring loudness or noisiness, segmenting into distinct elements). Thanks to widely available computer and audio hardware combined with various software packages, it is possible to work with these techniques in real-time systems, which enable direct feedback for the user of the system. After establishing some basics of how these techniques are implemented, we will be focusing on building or reengineering our own practical systems used in medical diagnosis or linguistics, with the goal of presenting these systems as working demonstrations or interactive installations connected with an end of semester poster presentation. While we will cover theoretical concepts, this will be a hands-on course, where we will work to develop projects.
The goal of this seminar is to gain research skills and to become familiar with the writing and research process. We will be dealing with the general steps of the research process, with conducting research using the tools and institutions available at our university, as well as with formal aspects of academic writing such as citations, formatting, structure of argumentation, and plagiarism. As academic conventions differ between institutes and departments, this course focuses on linguistic research in particular, and will provide the skills that are necessary for your term papers and BA dissertation.
Have you ever been curious about why Voice Assistants (Siri, Amazon Echo and Google Assistant) sometimes struggle to understand your English accent? As linguists, we can play a crucial role in evaluating these sophisticated systems. In this class, you will assess these voice assistants by evaluating them against your own recorded speech. We will start by using the recording lab to record your voice and create a dataset. We will then use the voice assistants over this dataset. Once we know the possible errors from these systems, we will find the source of the errors using phonological analysis.
Is technology really as innocent and as objective as they are said to be? As machine learning (ML) and Artificial Intelligence (AI) becomes more prominent in our life from speech and voice recognition by Alexa to automatic fake news warnings of social media posts, issues with social bias and fairness in language technology become more pertinent than ever before. Negative impacts that biased ML and AI could have for various social identities such as race, gender and culture. Through research papers, we will gain a better understanding of the the ethics, fairness, bias-related challenges in Natural Language Processing. Throughout the course, students will gain an overview of the various types of Natural Language Processing models (e.g., Automatic Speech Recognition, Language Model, hate speech detection) and its implications for bias, diversity, Inclusion, Environmental and human costs, privacy and governance.
This colloquium is designed for students working on term papers, Bachelor's, Master's, or PhD theses who are seeking feedback and support for their ongoing research. We will meet in person every two weeks, with alternating weeks dedicated to group-based collaboration and peer exchange. In our first session, we will determine the presentation topics, which will form the structure of the colloquium program. Alongside project discussions, the in-person meetings will also cover key academic skills such as time management, hypothesis development, and critical reading.
This course provides an introduction to computer programming using Python, a high-level programming language widely used in both academic and industry contexts. Designed specifically for students in linguistics and related fields, the course integrates technical training with a group project: the design and implementation of a constructed language (conlang), with a particular emphasis on generating and analysing noncewords (e.g., wug words).
It is as necessary to be numerate as it is to be literate, but students in the field of humanities are often not as numerate as they are literate. They will need to evaluate evidence that are based on probability-based models or statistical results in many of the courses that they take in university, as they consider the efficacy of vaccination and the severity of the pandemic, as they begin to vote in local and national elections, as they search for employment on the job market after graduating, and so on. With an increasingly digital world filled with big data, a command of statistical reasoning is more important than ever. In this course, we will learn numeracy through linguistics, specifically through phonetics and phonology by learning to analyse the sounds of languages quantitatively.
This course will introduce you to the exciting world of Large Language Models (LLMs) and their applications in linguistic research. You will start by learning the basics of LLMs, including their architecture, capabilities, and limitations.
Laboratory Phonology is the approach to studying phonology--the sound patterns in language--by using experiments. In this course, we will be particularly interested in answering questions about phonology using data from phonetics. Students will work together to conduct an original experimental study. Skills required to conduct the study will be taught and practiced during the course. The basic outline of the study will be provided, but we will develop the details during the semester. Students will develop the experiment design, conduct recordings, take measurements, analyze data, and interpret results. Students will use equipment in the new Computational Phonetics lab in order to conduct the class experiment. Students should expect to work in collaboration with classmates, and active participation in every stage of the course is required for BN credit. Participation in class activities is expected and required.
Language and Health is a seminar course that explores the intersection between language and heatlh. Through research papers, we will gain a better understanding of how language plays a pivotal role in both assessing health conditions and effectively conveying vital health information. Throughout the course, students will delve into the various dimensions of language (spoken speech, written text, sociolinguistic features of non-standard varieties) and its implications for individual and public health.