University Syllabi

This class serves as an introduction to human language technology (HLT), an emerging interdisciplinary field that encompasses most subdisciplines of linguistics, as well as computational linguistics, natural language processing, computer science, artificial intelligence, psychology, philosophy, mathematics, and statistics. Content includes a combination of theoretical and applied topics such as (but not limited to) tokenization across languages, n-grams, word representations, basic probability theory, introductory programming, and version control.

This course explores how computers process human language. Key technologies emphasized include word similarity, computational morphology and syntax, topic modeling, machine translation, and speech recognition and synthesis. Students are expected to be familiar with basic linguistic notions like phoneme, morpheme, (syntactic) head, constituent, etc., and to be comfortable developing in the Python program language.

This course provides both practical and theoretical knowledge on how we can leverage speech processing technologies to build a conversational AI system. The course encompasses speech recognition, speaker recognition, speech synthesis, speech enhancement, speech translation, spoken dialogue systems, speech foundation models, and other speech and audio processing tasks. In practical sessions, students will learn to build functional speech recognition and synthesis systems or utilize existing large speech and language models and integrate them to create a speech interface using existing toolkits. The course will also present details of algorithms, techniques, evaluation metrics, and limitations of state-of-the-art speech systems. This course is particularly designed for students who want to learn how to process actual data for real-world applications, applying AI and machine learning techniques while also being aware of the current technology limitations.

The Speech Technologies subject proposes the acquisition of knowledge and the understanding of the different technologies that make up the automatic systems of human-machine interaction based on spoken language. The main objectives of the course are to achieve the learning outcomes and the acquisition of competences listed in the corresponding sections of this guide.
These approaches and objectives are aligned with some of the Sustainable Development Goals, SDG, of the 2030 Agenda (https://www.un.org/sustainabledevelopment/es/) and certain specific goals, in such a way that the acquisition of the Learning outcomes of the subject provides training and competence to the student to contribute to a certain extent to their achievement:
Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
Target 8.2 Achieve higher levels of economic productivity through diversification, technological modernization and innovation, including by focusing on high value-added and labor-intensive sectors.
Target 8.3 Promote development-oriented policies that support productive activities, the creation of decent jobs, entrepreneurship, creativity and innovation, and encourage the formalization and growth of micro, small and medium-sized enterprises, including through access to financial services.
Goal 9: Industry, innovation and infrastructure
Target 9.5 Increase scientific research and improve the technological capacity of industrial sectors in all countries, particularly developing countries, including by fostering innovation and significantly increasing, by 2030, the number of people working in research and development per million inhabitants and the spending of the public and private sectors in research and development.
Target 9.c Significantly increase access to information and communication technology and strive to provide universal and affordable access to the Internet in least developed countries by 2020.

This class will introduce students to spoken language processing: basic concepts, analysis approaches, and applications. Applications include Text-to-Speech Synthesis, dialogue systems, and analysis of entrainment, empathy, personality, emotion, humor and sarcasm, deception and trust, radicalization and charisma, all using text and speech information and some visual features as well.

As technology advances, we’re relying more and more on computers and ‘virtual assistants’ to understand and interact with us using human language. In this course, we’ll talk about the methods we use to help computers process, understand, analyze, and speak our language, and we’ll talk about some of the linguistic realities that make human language so hard for computers to work with. No programming or linguistic background is needed.

We will cover ethical and social issues in NLP, especially focusing on large language models/foundation models. We’ll cover research ethics, IRB, data and participatory research, harms and their mitigation, what models we should build and who should decide, and how to apply NLP to social questions about the justice system, framing and dehumanization, disinformation and toxicity, and for counter speech.

An enduring aspect of the quest to build intelligent machines is the challenge of human language. This course introduces students with a background in computer science and a research interest in artificial intelligence fields to the structure of natural language, from sound to society. It covers phonetics (the physical aspects of speech), phonology (the sound-structure of language), morphology (the structure of words), morphosyntax (the use of word and phrase structure to encode meaning), syntactic formalisms (using finite sets of production rules to characterize infinite configurations of structure), discourse analysis and pragmatics (language in discourse and communicative context), and sociolinguistics (language in social context and social meaning). Evaluation is based on seven homework assignments, a midterm examination, and a final examination.

This undergraduate class looks at everyday tasks that involve natural language processing: document classification, spelling and grammar correction, dialogue systems, machine translation, cryptography and forensic linguistics. Students will get insight into how these systems work (and why it is still so difficult to do natural language processing well). We also consider social and ethical considerations such as privacy, job creation and loss due to language technologies, and the nature of machine intelligence.

Language technology has a profound influence on the way ordinary people use language. This morning, because you speak English, you may have already used voice recognition or predictive text. This course explains what language technology is and how it is available for 1% of the world’s languages. Along the way, we will attempt to answer a big pressing question for our society: Can artificial intelligence ever be inclusive of all 7000+ human languages? We will identify, describe, and explain the cross-disciplinary dimensions of this question which lie partly in social science principles of human communication (e.g. conversational turn-taking), partly in linguistics theory (e.g. how languages build words), and partly in the history of computer science (e.g. ASCII vs. Unicode). Topics include spellcheckers, translation, chatbots, and language learning aids. Topics are explored in the context of globalization, language endangerment, and the recent rapid rise of artificial intelligence.

The goal of this course is to better understand the ethical considerations that arise in the deployment of NLP technology, including how to identify people likely to be impacted by the use of the technology (direct and indirect stakeholders), what kinds of risks the technology poses, and how to design systems in ways that better support stakeholder values. Through discussions of readings in the growing research literature on fairness, accountability, transparency and ethics (FATE) in NLP and allied fields, and value sensitive design, we will seek to answer the following questions:
What can go wrong, when we use NLP systems, in terms of specific harms to people?
How can fix/prevent/mitigate those harms?
What are our responsibilities as NLP researchers and developers in this regard?

Speech (naturally spoken) is the main mode of communication between humans. Speech technology aims at providing the means for speech-controlled man-machine interaction. The goal of this course is to provide the basic concepts and theories of speech production, speech perception, and speech signal processing. The course is organized in a manner that builds a strong foundation of basics, followed by a range of signal processing methods for representing and processing the speech signal.

Speech technology includes computer speech recognition and computer speech synthesis. This hands-on
course begins with a review of basic phonetics and speech signal processing and then goes through the basic
logic and methodologies for speech synthesis and recognition. Programming expertise is not required, but
most assignments will have programming options for those interested.

Introduction to spoken language technology with an emphasis on dialog and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems.