Lecture Notes ( Lecture Slides or PPTs ) on Speech Signal Processing by Dr. Rajesh M. Hegde sir

 Lecture Slides (PPTs) from IIT Professors :

Speech Signal Processing by Dr. Rajesh M. Hegde sir

Dr. Rajesh M. Hegde

He is currently Associate Professor and P K Kelkar Research Fellow in the Dept. of Electrical Engineering at IIT Kanpur. His areas of interest include speech, audio, vision, multimodal information fusion, and applications of signal processing in wireless sensor networks. He also has considerable interests in pervasive computing. Prior to this, he was a researcher and lecturer  at the California Inst. of Telecommunications and Information Technology (University of California San Diego) and associated with the DSP Lab .

Areas of Interest:

• Speech Signal Procesing and Recognition (with emphasis on Indian Languages)
  
• Speaker and Language Identification
  
• Affective Speech Processing
  
• Multi modal signal processing
  
• Pervasive and Ubiquitous Computing
  
• Applied Signal Processing in wireless networks
  
• IT in Emergency Response

Contact Details :

Tel:  +91-512-2596248 (Work)
0-9793700555 (Cell)Fax:  +91-512-2590063
Email:  rhegde[AT]iitk.ac.in

Topic
1/2		Overview of speech recognition, Modeling the speech production mechanism, Source-system model of speech, Physiological and Mathematical categorization of speech sounds
1/2		Overview of speech recognition, Modeling the speech production mechanism, Source-system model of speech, Physiological and Mathematical categorization of speech sounds
3/4		Discrete time processing of speech signals, Relevance of the DFT, the ZT, convolution, filter banks, and analytical pole-zero modeling in speech recognition
3/4		Discrete time processing of speech signals, Relevance of the DFT, the ZT, convolution, filter banks, and analytical pole-zero modeling in speech recognition
3/4		Discrete time processing of speech signals, Relevance of the DFT, the ZT, convolution, filter banks, and analytical pole-zero modeling in speech recognition
3/4		Discrete time processing of speech signals, Relevance of the DFT, the ZT, convolution, filter banks, and analytical pole-zero modeling in speech recognition
5/6		Short time Fourier Analysis and Spectral estimation models for Speech - DTFT, DFT, Filter banks
5/6		Short time Fourier Analysis and Spectral estimation models for Speech - DTFT, DFT, Filter banks
5/6		Short time Fourier Analysis and Spectral estimation models for Speech - DTFT, DFT, Filter banks
7/8		Pole zero modeling and All pole modeling of speech, LPC model for speech, Basics of Speech Coding
Q1		Quiz 1
7/8		Pole zero modeling and All pole modeling of speech, LPC model for speech, Basics of Speech Coding
7/8		Pole zero modeling and All pole modeling of speech, LPC model for speech, Basics of Speech Coding
7/8		Pole zero modeling and All pole modeling of speech, LPC model for speech, Basics of Speech Coding
7/8		Pole zero modeling and All pole modeling of speech, LPC model for speech, Basics of Speech Coding
7/8		Pole zero modeling and All pole modeling of speech, LPC model for speech, Basics of Speech Coding
11/12		Homomorphic speech signal deconvolution, real and complex cepstral analysis
11/12		Homomorphic speech signal deconvolution, real and complex cepstral analysis
13/14		Features for speech recognition: MFCC, RASTA-PLP, Issues in speech feature vector extraction, dynamic features, feature selection
13/14		Features for speech recognition: MFCC, RASTA-PLP, Issues in speech feature vector extraction, dynamic features, feature selection
13/14		Features for speech recognition: MFCC, RASTA-PLP, Issues in speech feature vector extraction, dynamic features, feature selection
13/14		Features for speech recognition: MFCC, RASTA-PLP, Issues in speech feature vector extraction, dynamic features, feature selection
15/16		Spectral and cepstral distances in speech recognition, Vector Quantization
15/16		Spectral and cepstral distances in speech recognition, Vector Quantization
15/16		Spectral and cepstral distances in speech recognition, Vector Quantization
Q2		Quiz 2
17/18		GMMs for speaker and Language Identification
17/18		GMMs for speaker and Language Identification
DTW		Dynamic Time Warping  (The slides will be emailed to all registered students)
DTW		Dynamic Time Warping
DTW		Dynamic Time Warping
HMM		Hidden Markov Models for isolated word and continuous speech recognition - Part I
HMM		Hidden Markov Models for isolated word and continuous speech recognition - Part I
HMM		Hidden Markov Models for isolated word and continuous speech recognition - Part II
HMM		Hidden Markov Models for isolated word and continuous speech recognition - Part II
SP***		Student Project Presentations