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FDT3151 Conversational Systems 7.5 credits

Conversational systems allow users to interact with machines through spoken or written language. Examples include smart speakers, voice assistants, chatbots, and social robots. This involves the use of technologies such as natural language processing, speech technology, and multi-modal interfaces. The course will give an in-depth understanding of conversational systems, from both a theoretical and practical perspective, through lectures, exercises and a project. Both classical rule-based approaches, as well as more recent deep learning approaches will be covered.

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Headings with content from the Course syllabus FDT3151 (Autumn 2020–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course consists of five lectures, three laboratory sessions, and a final project done individually or in groups. The project is presented at a project pitch and during a final seminar. In the laboratory sessions, we will use different platforms for building conversational systems (including graphical tools, social robotics and deep learning).

The lectures will cover the following topics:

  • Introduction to conversational systems. Historical perspective. Overview of speech and language technlogy components.
  • Theories of conversation. Semantics and pragmatics of dialog. Psycholinguistic perspectives.
  • Natural language understanding. Grammar-based and data-driven approaches. Visual language grounding.
  • Dialog management. Chat- and task-oriented dialog. Rule-based and neural models. Reinforcement learning.
  • Multi-modal conversation, Social signals and Social robotics.

Intended learning outcomes

After the course, the student should be able to:

  • Describe different state-of-the-art models for conversational systems, their weaknesses and strengths, and apply a suitable model depending on the application
  • Implement a conversational system using rule-based frameworks and deep learning
  • Identify research topics related to conversational systems, find relevant litterature, develop a research plan, and execute the research project

Course disposition

5 lectures, 3 labs, 2 project seminars

Literature and preparations

Specific prerequisites

Doctoral students from EECS

Recommended prerequisites

Some knowledge of Machine learning is recommended (e.g. DD2421, DD2434, EN2202)
Some programming knowledge is required.


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Compilation with relevant articles.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • EXA1 - Written examination, 7.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

All lectures, excercises and project seminar attended. Approved project report.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FDT3151

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted


Gabriel Skantze (

Postgraduate course

Postgraduate courses at EECS/Speech, Music and Hearing