UCLA Dept. of Electrical Engineering EE214A: Digital Speech Processing Problem Set 7 (LAST ONE!) Due: 3/2/2011 Reading Assignment: Chapter 8.
1. Suppose that using LPC you estimated the transfer function to be:
H (z ) = 1=1 ; 2z ;1 ; 6z ;2 + z ;3 ; 2z ;4
UCLA Dept. of Electrical Engineering EE M214A, Winter 2011 Problem Set 4 Solution
February 7, 2011
1. 7.13 Answer:
1
2
2. 7.14 Answer:
3
4
5
3. 7.18 Answer:
6
4. Answer: Omitted. 5. (a) To avoid aliasing in the time domain, the number lters must be at lea
Lecture 13
EE214A
Abeer Alwan
Speech Application
p
In speech, we assume
s(n) ak s(n k ) Gu (n)
Transfer function is
S ( z)
U ( z)
k 1
G
p
1 ak z k
G
A( z )
k 1
Goal: Estimate aks
p
Assume prediction signal is
~
s ( n ) ak s ( n k )
k 1
Prediction err
Lecture 12
EE214A
Abeer Alwan
Some Applications
LPC10 speech coder
The SIFT algorithm
1
Application Example:
Pitch-Excited LPC Transmitter
Block Diagram of a
Pitch-Excited LPC Receiver
2
Direct Form Implementation of the
Vocal Tract Filter
Speech Coding
Lecture 11
EE214A
Abeer Alwan
Linear Prediction Coding
(LPC)
1
Historical Overview
Analysis of the outputs of dynamical
systems has lead to time-series: analysis
especially in
Statistics
Economics
Communication systems
Used in system identification a
UCLA
Dept. of Electrical Engineering
EE214A
Problem Set 4
Due: 02/11/2015
Reading Assignment: Rabiner and Schafer: Chapter 7: Sections 7.5-7.7,
7.11.3-end of Chapter 7.
1. Go to one of the TTS demo sites. Test out the site by choosing three dierent
senten
UCLA
Dept. of Electrical Engineering
EE214A
Problem Set 3
Due: 2/01/2016
Reading Assignment: Rabiner and Schafer: Ch. 6 except Section 6.3.1.
1. Problem 6.7.
2. Problem 6.13.
3. Problem 6.16.
1
4. The conguration shown in the gure below is an idealized vo
Lecture 8
EE214A
Abeer Alwan
Signal Reconstruction
Filterbank Summation (FBS)
Overlap and Add (OLA)
1
Recall that the Short Time Fourier Transform
(STFT) is defined as:
X n (e j ) x(m) w(n m)e jm
m
an ( ) jbn ( )
| X n (e j ) | e j n ( )
Second Interpr
Lecture 14
EE214A
Abeer Alwan
Recall the LTI Model of Speech Production
1
Analysis Techniques Studies Thus
Far
We have studied how to analyze the speech
signal using time-domain features and by
using the STFT and LPC
The STFT provides temporal-spectral
Lecture 3
EE 214A
Abeer Alwan
Last Time
Transforms
Properties of the DFT
LTI systems
Relationship between CT and DT
signals/transforms
Filtering
Up/Down Sampling
1
Today
Sounds of American English
Terminology used in speech processing
LTI model of speec
Lecture 4
EE214A
Abeer Alwan
(some slides are from Prof. Rabiner)
Last Time
Classification of sounds, and mathematical
models of the source/excitation: function
with is voiced (with and without noise), or
voiceless. Voicing could be modeled as an
impulse
EE214A Lecture 2
Abeer Alwan
Last Time
Frequency Transforms for CT and DT signals:
X ( s), X ( j), ck ; X ( z ), X (e j ), X (k ), ck
Periodic signals have a line spectrum while
aperiodic signals have a continuous spectrum.
Also recall that:
/ T where T
EE214A Digital Speech Processing
Lecture 1
Abeer Alwan
all rights reserved
Denes and Pinson (1963)
1
Disciplines involved in Speech
Research
Engineering and Computer Science
Linguistics (phonetics, phonology, semantics,
syntax, etc.)
Psychology, Biolog
Lecture 5
Text to Speech Synthesis (TTS)
and Time Domain Processing
(some slides are courtesy of Prof.
Larry Rabiner)
1
Wavesurfer Demo
Download software from:
www.speech.kth.se/wavesurfer/download.html
Time and Frequency Domain Analysis: pitch
estimation
Lecture 6: The STFT
EE214A
Abeer Alwan
The Short Time Fourier Transform (STFT)
X n (e j ) x(m) w(n m)e jm
m
an ( ) jbn ( )
| X n (e j ) | e j n ( )
w(n-m) is a real window that determines the portion of the signal
to be analyzed. The STFT is sometimes wr
Introduction to Automatic
Speech
Recognition Systems (ASR)
Abeer Alwan
Speech Processing and Auditory Perception Laboratory
Speech Recognition System Overview
Model
Training
Speech
Signal
Acoustic
Model
Feature
Extraction
Text
Output
Decoding
Network
Dict
1/23/2014
Lecture 7
EE214A
Abeer Alwan
Mystery Spectrogram
Ul
Choose between: Enjoy, was, that, and, by, people,
little, simple, between, very,
those
1
1/23/2014
The Short Time Fourier Transform (STFT)
X n (e j ) x(m) w(n m)e jm
m
an ( ) jbn ( )
| X n (e
UCLA
Dept. of Electrical Engineering
EE214A (online)
Problem Set 2
Due: 01/25/2016
Reading Assignment: Chapter 3 and Chapter 5 (except for Sections 5.1.3,
5.1.4, 5.2.3, and 5.2.4).
1. Transcribe your rst and last name phonetically. Then, analyze the sound
UCLA
Dept. of Electrical Engineering
EE214A
Problem Set 5
Due: 2/22/2016
Reading Assignment: Rabiner and Schafer: Chapter 4 except for Section
4.5. Chapter 9 till Section 9.4.
1. Problem 4.3.
2. Problem 4.4.
3. Problem 9.1
4. Four consecutive samples of a
UCLA
Dept. of Electrical Engineering
EE214A
Problem Set 1
Due: 01/13/2016
Reading Assignment: Chapters 1 and 2.
1. A signal xa (t) is band limited to 10 kHz and was sampled at 20 kHz. A DFT
of size N=1000 was then computed.
(a) What is the spacing between
UCLA Dept. of Electrical Engineering EE214A: Digital Speech Processing Problem Set 3 Due: 1/31/2011 Reading Assignment: Chapters 6 and 7 (till Section 7.5).
1. The con guration shown in the gure below is an idealized vocal tract during the production of t
UCLA Dept. of Electrical Engineering EE214A: Digital Speech Processing Problem Set 4 Due: 2/7/2011 Reading Assignment: Chapter 7: Sections 7.5-7.7, 7.11.3-end of Chapter 7.
1. Problem 7.13. 2. Problem 7.14. 3. Problem 7.18. 4. Go to one of the TTS demo si
UCLA Dept. of Electrical Engineering EE M214A, Winter 2011 Problem Set 5 Solution
February 16, 2011
1. Answer:
2. Answer:
1
3. Answer:
4. Answer: (a) The autocorrelation function is given by:
N 1
R (i) =
n=0 2
x (n) x (n i)
R (0) = 3 + 22 + (1)2 + 12 = 15
UCLA Dept. of Electrical Engineering EE214A: Digital Speech Processing Problem Set 5 Due: 2/16/2011 Reading Assignment: Chapter 4 except for Section 4.5. Chapter 9: till Section 9.4 1. Problem 4.3. 2. Problem 4.4. 3. Problem 4.5. 4. Four consecutive sampl
UCLA Dept. of Electrical Engineering EE214A: Digital Speech Processing Problem Set 6 Due: 2/23/2011 Reading Assignment: Continue reading Chapter 9 till 9.5.3, then 9.6, 9.7 (skip 9.7.3), and 9.9. 1. Problem 9.1. 2. Problem 9.7 (you only need to do the aut
Lecture 16B
EE214A
Abeer Alwan
Coding
Coding is a 2-stage process: quantization and
encoding
Quantization is how one divides the dynamic,
amplitude, range into a finite set of ranges
Uniform quantizers assign, for example, B bits
with a certain quantiz
More on ASR
Abeer Alwan
Speech Processing and Auditory Perception Laboratory
Automatic Speech Recognition
Tasks
Isolated Word
Connected Word
Continuous Speech
Spontaneous Speech
Vocabulary Size
Small Vocabulary ( <100 )
Medium Vocabulary ( 100 500
A New Set of Features for Text-Independent Speaker Identification
Carol Y. Espy-Wilson, Sandeep Manocha and Srikanth Vishnubhotla
Institute for Systems Research and Dept. of Electrical & Computer Engineering,
University of Maryland, College Park, MD, USA
Lecture 10
EE214A
Abeer Alwan
Midterm Review
Material covered: PS1 thru PS4, assigned book readings, lectures
1-10 (excluding 9), and other reading material posted. One cheat
sheet (8.5 x 11, double sided) is allowed. No calculators, phones,
or other elec
EE214A Digital Speech Processing
Lecture 18B (Review)
Abeer Alwan
all rights reserved
1
Focus of this course
Acoustic Phonetics
LTI model of speech production
STFT (used in most speech processing
applications especially in speech analysis)
LPC (used p
UCLA
Dept. of Electrical Engineering
EE214A Winter 2016
Project Description
The project report, oral presentations, and evaluations are scheduled for Wednesday, March , from 10:00 11:30 a.m. in class.
1. Introduction
In this project, we are interested in