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Speech Tech

Syllabus, study tools, lectures, and curriculum map.

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Review of Signals and Systems, Continuous time signals and transforms

Incomplete

02W02

Acoustic Feature Analysis of Speech Signals I, II

Incomplete

03W03

Hidden Markov model (HMM), Examples of HMM based approach for ASR, TTS, speaker diarization

Incomplete

04W04

Introduction and History of ASR and TTS

Incomplete

05W05

HMMs for Acoustic Modelling - Monophone, Triphone

Incomplete

06W06

Neural networks for building speech technologies

Incomplete

07W07

End-to-End Approaches I:

Incomplete

08W08

Applications to ASR and TTS

Incomplete

09W09

Encoder-decoder Architecture E2E with transformers for ASR and TTS

Incomplete

010W10

Speaker recognition/verification: with ivector, xvector

Incomplete

011W11

Speaker adaptation: (revisit i, x vectors) and introduce s-vectors.

Incomplete

012W12

Singing voice synthesis; voice conversion; generic voice synthesis

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

2 min

Learning Objectives

Open this first for the core logic and traps.

Dive in

2 min

Learning Objectives

Open this first for the core logic and traps.

Dive in

2 min

Learning Objectives

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // Playlist Connected

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSEE4001

BS Degree

4 Credits

Speech Technology

The following are the suggested books for the course:

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Review of Signals and Systems, Continuous time signals and transforms

Module 2

Acoustic Feature Analysis of Speech Signals I, II

Module 3

Hidden Markov model (HMM), Examples of HMM based approach for ASR, TTS, speaker diarization

Module 4

Introduction and History of ASR and TTS

Module 5

HMMs for Acoustic Modelling - Monophone, Triphone

Module 6

Neural networks for building speech technologies

Module 7

End-to-End Approaches I:

Module 8

Applications to ASR and TTS

Module 9

Encoder-decoder Architecture E2E with transformers for ASR and TTS

Module 10

Speaker recognition/verification: with ivector, xvector

Module 11

Speaker adaptation: (revisit i, x vectors) and introduce s-vectors.

Module 12

Syllabus

Topic

Review of Signals and Systems, Continuous time signals and transforms

Acoustic Feature Analysis of Speech Signals I, II

Hidden Markov model (HMM), Examples of HMM based approach for ASR, TTS, speaker diarization

Introduction and History of ASR and TTS

HMMs for Acoustic Modelling - Monophone, Triphone

Neural networks for building speech technologies

End-to-End Approaches I:

Applications to ASR and TTS

Encoder-decoder Architecture E2E with transformers for ASR and TTS

Speaker recognition/verification: with ivector, xvector

Speaker adaptation: (revisit i, x vectors) and introduce s-vectors.

Singing voice synthesis; voice conversion; generic voice synthesis

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Speech Technology

Execution Protocol

Topic

Review of Signals and Systems, Continuous time signals and transforms

Acoustic Feature Analysis of Speech Signals I, II

Hidden Markov model (HMM), Examples of HMM based approach for ASR, TTS, speaker diarization

Introduction and History of ASR and TTS

HMMs for Acoustic Modelling - Monophone, Triphone

Neural networks for building speech technologies

End-to-End Approaches I:

Applications to ASR and TTS

Encoder-decoder Architecture E2E with transformers for ASR and TTS

Speaker recognition/verification: with ivector, xvector

Speaker adaptation: (revisit i, x vectors) and introduce s-vectors.

Singing voice synthesis; voice conversion; generic voice synthesis

Video Archive

Intelligence Hub