Digital Processing of Speech Signals
CMPE Degree: This course is Not Applicable for the CMPE degree.
EE Degree: This course is Not Applicable for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Digital Signal Processing
Course Coordinator: Chin-Hui Lee
Catalog DescriptionThe application of digital signal processing to problems in speech
communication. Part of this goal requires a laboratory project.
Student OutcomesIn the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.
1. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2. ( Not Applicable ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3. ( Not Applicable ) An ability to communicate effectively with a range of audiences
4. ( Not Applicable ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5. ( Not Applicable ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Strategic Performance Indicators (SPIs)
Outcome 1 (Students will demonstrate expertise in a subfield of study chosen from the fields of electrical engineering or computer engineering):
1. Using tools learned in class to display the speech waveform and its corresponding spectrogram, pitch contour, energy progression and format profile.
2. Deriving cues from above-mentioned time-frequency features can be used to further infer key information about the speaker, speaking environment and even the linguistic content.
Outcome 2 (Students will demonstrate the ability to identify and formulate advanced problems and apply knowledge of mathematics and science to solve those problems):
Outcome 3 (Students will demonstrate the ability to utilize current knowledge, technology, or techniques within their chosen subfield):
1. Given a project at work, the students should be able to carry out literature survey, problem formulation, speech data collection, speech experiments, and project reports by relating real-world signal processing problems to what they learn in class and analyze their experimental results by varying key parameters
1. Introduction to Speech Communication
a. Concept of Speech Knowledge Hierarchy
b. Distinctive Features and Derived Phonetic Structure
c. Supersegmental Description of Speech
2. The Vocal Mechanism and Sound Acoustics
a. Electrical Analog of the Vocal Tract
b. Two Tube Model for Vowels
c. Fricatives, Glides, Liquids and Stops
3. Review of Digital Signal Processing
a. Fourier Transforms
c. System Functions
4. Digital Models for Speech Production
a. Acoustics of Speech Production, Properties of Speech Waveform
b. Digital Models and Basic Problems of Speech Processing
5. Speech Waveform Fundamentals
a. Sampling Theorem and Quantization
b. Mu-law, A-law and Optimum Quantization
6. Time-Domain Analysis Methods
a. Peak, Energy and Zero-crossing Measurements
b. Auto-correlation Analysis
7. Short-Time Spectrum Analysis Methods
a. Definitions, Filterbanks, Computation, Sound Spectrograms
b. Decimation and Interpolation
8. Cepstrum and Homomorphic Speech Processing
a. Basic Theory, Cepstrum of Speech Signals
b. Pitch Detection, Formant Analysis and Applications
9. Linear Prediction Analysis
a. Basic Theory and Implementations
b. Formant Analysis, Spectrum Analysis
c. Lattice Structures,Recursive Autocorrelation Functions
10. Introduction to Speech Coding
11. Introduction to Speech Synthesis and Vocoders
12. Introduction to Automatic Speech and Speaker Recognition