| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | PSO3 | ||
| K3 | K4 | K5 | K5 | K6 | - | - | - | - | - | - | - | K5 | K3 | K6 | ||
| CO1 | K2 | 2 | 2 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 2 | 1 |
| CO2 | K3 | 3 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 2 | 3 | 1 |
| CO3 | K3 | 3 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 2 | 3 | 1 |
| CO4 | K3 | 3 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 2 | 3 | 1 |
| CO5 | K2 | 2 | 2 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 2 | 1 |
| Score | 13 | 10 | 8 | 0 | 5 | 0 | 0 | 5 | 5 | 5 | 0 | 5 | 8 | 13 | 5 | |
| Course Mapping | 3 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 2 | 3 | 1 |
{{{credits}}}
| L | T | P | C |
| 3 | 0 | 0 | 3 |
- To explore the fundamentals of digital speech processing
- To understand the basic concepts and algorithms of speech processing
- To be familiar with the various speech signal representation, coding and recognition techniques
- To study the concepts and evaluation methods of speech synthesis.
{{{unit}}}
| UNIT I | FUNDAMENTALS OF DIGITAL SPEECH PROCESSING | 9 |
Introduction: Discrete-time signals and systems – Transform representation of signals and systems – Fundamentals of digital filters – Sampling; Process of speech production – Acoustic theory of speech production – Digital models for speech signals.
{{{unit}}}
| UNIT II | SPEECH SIGNAL ANALYSIS IN TIME DOMAIN | 9 |
Time-dependent processing of speech – Methods for extracting the Parameters: Energy – Average magnitude – Zero-crossing rate; Silence discrimation using ZCR and energy – Short-time autocorreleation function – Pitch period estimation using autocorrelation function.
{{{unit}}}
| UNIT III | SPEECH SIGNAL ANALYSIS IN FREQUENCY DOMAIN | 9 |
Short time fourier analysis – Fourier transform and linear interpretations – Sampling rates – Spectrographic displays – Formant extraction – Pitch extraction – Linear predictive coding: Autocorrelation method – Covariance method; Solution of LPC equations – Durbin’s Recursive solution – Application of LPC parameters – Pitch detection.
{{{unit}}}
| UNIT IV | SPEECH RECOGNITION | 9 |
Introduction – Preprocessing – Parametric representation – Speech segmentation – Dynamic time warping – Vector quantization – Hidden Markov Model – Language Models – Developing an isolated digit recognition system.
{{{unit}}}
| UNIT V | SPEECH SYNTHESIS | 9 |
Attributes of speech synthesis – Formant speech synthesis – Concatenative speech synthesis – Prosodic modification of speech – Source filter models for prosody modification – Evaluation of TTS system.
\hfill Total Periods: 45
After the completion of this course, students will be able to:
- Illustrate how the speech production is modelled (K2)
- Extract features from the speech signal in time domain (K3)
- Analyze the speech signal in frequency domain (K3)
- Develop a speech recognition system using statistical approach (K3)
- Compare the various methods of speech synthesis (K2).
- L R Rabiner, R W Schafer, “Digital Processing of Speech Signals”, Pearson Education, Delhi, India, 2004.
- Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, “Spoken Language Processing – A guide to Theory, Algorithm and System Development”, Prentice Hall PTR, 2001.
- L R Rabiner, B H Jhuang, B Yegnanarayana, “Fundamentals of Speech Recognition”, Pearson Education, 2009.
- Thomas F Quatieri, “Discrete-Time Speech Signal Processing”, Pearson Education, 2002.
- Ben Gold, Nelson Morgan, “Speech and Audio Signal Processing”, John Wiley and Sons Inc, 2004.
- J R Deller Jr, J H L Hansen, J G Proakis, “Discrete-Time Processing of Speech Signals”, Wiley-IEEE Press, NY, USA, 1999.
- Daniel Jurafsky, James H Martin, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”, 2nd edition, Pearson education, 2013.