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DT2119 Speech and Speaker Recognition 7.5 credits

The course objective is to provide a systematic introduction to speech processing and recognition. Models of speech production and speech analysis will form a basis to understanding the problem of speech recognition. Probabilistic machine learning methods will be employed for the recognition task, including Hidden Markov Models, Gaussian Mixture Models, Support Vector Machines, Deep Neural Networks.

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For course offering

Spring 2025 Start 17 Mar 2025 programme students

Application code


Headings with content from the Course syllabus DT2119 (Spring 2020–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course consists of lectures, three laboratory sessions with hand-in assignments, as well as writing an essay on a subject chosen in consultation with the teacher. The thesis is furthermore presented orally during a final seminar. The laboratory sessions consist of designing different parts of a speech recognition application, training the system and evaluating its performance.

The following theoretical course components are included:

  • algorithms for training, recognition as well as adaptation to properties of speakers and transmissions channel, including pattern recognition, Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs)
  • methods to decrease the sensitivity to disturbances and deviations
  • probability theory
  • signal processing and parameter extraction
  • acoustic modelling of the static and dynamic spectral properties of speech sounds
  • statistical modelling of language in spontaneous and formal speech
  • search strategies - basic methods and strategies for large vocabularies
  • specific methods for analysis and decision making, for recognition of speakers.

Furthermore, some practical insights into building an application are given. This includes the implementation of certain functions based on prototypes, and testing them on real speech data.

Intended learning outcomes

Having passed the course, the student shall be able to

  • implement methods for training and evaluation of speech recognition systems
  • train and evaluate a speech recognizer, using software tools
  • compare different methods for feature extraction and training
  • document and discuss specific aspects related to recognition of speech and of speakers
  • review and criticise other students' work in the subject, based on the literature.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

Some knowledge of Machine learning, possibly DD2421, DD2434 or EN2202

Some programming knowledge, best if Python

Some knowledge in Signal Processing


No information inserted


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Examination and completion

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

Grading scale

A, B, C, D, E, FX, F


  • LAB1 - Computer Lab, 4.5 credits, grading scale: P, F
  • PRO1 - Project, 3.0 credits, grading scale: A, B, C, D, E, FX, 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

Laboratory exercises
Written assignments.
Academic paper and its presentation at a final review
Assessment of two other course participants' theses, and critical review of their presentations.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


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 room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted


Jonas Beskow (

Supplementary information

The course may be canceled or be given in another form if the number of regular registrations are too few.

In this course, the EECS code of honor applies, see: