Speech Processing AEGIS RET All-Hands Meeting University of Central Florida July 20, 2012 Applications of Images and Signals in High Schools

Contributors Dr. Veton Këpuska, Faculty Mentor, FIT Jacob Zurasky, Graduate Student Mentor, FIT Becky Dowell, RET Teacher, BPS Titusville High

Speech Processing Project Speech recognition requires speech to first be characterized by a set of “features” Features are used to determine what words are spoken. Our project implements the feature extraction stage of a speech processing application.

Timeline 1874: Alexander Graham Bell proves frequency harmonics from electrical signal can be divided 1952: Bell Labs develops first effective speech recognizer DARPA: speech should be understood, not just recognized 1980’s: Call center and text-to-speech products commercially available 1990’s: PC processing power allows use of SR software by ordinary user Timeline of Speech Recognition.

Applications Call center speech recognition Speech-to-text applications (e.g. dictation software) Hands-free user-interface (e.g., OnStar, XBOX Kinect, Siri) – Science Fiction 1968: Stanley Kubrick’s 2001: A Space Odyssey – Science Fact 2011: Apple iPhone 4S Siri Medical Applications – Parkinson’s Voice Initiative – Detection of Sleep Disorders

Difficulties Continuous Speech (word boundaries) Noise – Background – Other speakers Differences in speakers – Dialects/Accents – Male/female

Speech Recognition Front End: Pre-processing Back End: Recognition Speech Recognized speech Large amount of data. Ex: 256 samples Features Reduced data size. Ex: 13 features Front End – reduce amount of data for back end, but keep enough data to accurately describe the signal. Output is feature vector. 256 samples > 13 features Back End - statistical models used to classify feature vectors as a certain sound in speech

Front-End Processing of Speech Recognizer Pre- emphasis High pass filter to compensate for higher frequency roll off in human speech

Front-End Processing of Speech Recognizer Pre- emphasis Window High pass filter to compensate for higher frequency roll off in human speech Separate speech signal into frames Apply window to smooth edges of framed speech signal

Front-End Processing of Speech Recognizer Pre- emphasis Window FFT High pass filter to compensate for higher frequency roll off in human speech Separate speech signal into frames Apply window to smooth edges of framed speech signal Transform signal from time domain to frequency domain Human ear perceives sound based on frequency content

Front-End Processing of Speech Recognizer Pre- emphasis Window FFT Mel-Scale High pass filter to compensate for higher frequency roll off in human speech Separate speech signal into frames Apply window to smooth edges of framed speech signal Transform signal from time domain to frequency domain Human ear perceives sound based on frequency content Convert linear scale frequency (Hz) to logarithmic scale (mel-scale)

Front-End Processing of Speech Recognizer Pre- emphasis Window FFT Mel-Scale log High pass filter to compensate for higher frequency roll off in human speech Separate speech signal into frames Apply window to smooth edges of framed speech signal Transform signal from time domain to frequency domain Human ear perceives sound based on frequency content Convert linear scale frequency (Hz) to logarithmic scale (mel-scale) Take the log of the magnitudes (multiplication becomes addition) to allow separation of signals

Front-End Processing of Speech Recognizer Pre- emphasis Window FFT Mel-Scale log IFFT High pass filter to compensate for higher frequency roll off in human speech Separate speech signal into frames Apply window to smooth edges of framed speech signal Transform signal from time domain to frequency domain Human ear perceives sound based on frequency content Convert linear scale frequency (Hz) to logarithmic scale (mel-scale) Take the log of the magnitudes (multiplication becomes addition) to allow separation of signals Inverse of FFT to transform to Cepstral Domain… the result is the set of “features”

Speech Analysis and Sound Effects (SASE) Project Graphical User Interface (GUI) Speech input – Record and save audio – Read sound file (*.wav, *.ulaw, *.au) Graphs the entire audio signal Process user selected speech frame and display output for each stage of processing Displays spectrogram Apply audio effects

MATLAB Code Graphical User Interface (GUI) – GUIDE (GUI Development Environment) – Callback functions Front-end speech processing – Modular functions for reusability – Graphs display output for each stage Sound Effects – Echo, Reverb, Flange, Chorus, Vibrato, Tremolo, Voice Changer

GUI Components

Plotting Axes

GUI Components Plotting Axes Buttons

SASE Lab Demo Record, play, save audio to file, open existing audio files Select and process speech frame, display graphs of stages of front-end processing Display spectrogram for entire speech signal or user selectable 3 second sample Play speech – all or selected 3 sec sample Show differences in certain sounds in spectrogram and the features ex: “a e i o u” so audience understands how these graphs tell us about the sounds Apply sound effects, show user configurable parameters Graphs spectrogram and speech processing on sound effects – Show echo effect in spectrogram Use as teaching tool

Future Work on SASE Lab Audio Effects – Ex: Pitch removal Noise Filtering

Applications of Signal Processing in High Schools Convey the relevance and importance of math to high school students Bring knowledge of engineering, technological innovation, and academic research into high school classrooms Opportunity for students to acquire technical knowledge and analytical skills through hands-on exploration of real-world applications in the field of Signal Processing Encourage students to pursue higher education and careers in STEM fields

Unit Plan: Speech Processing Collection of lesson plans introduce high school students to fundamentals of speech and sound processing Connections to Pre-Calculus mathematics standards (NGSSS and Common Core) – Mathematical Modeling – Trigonometric Functions – Complex Numbers in Rectangular and Polar Form – Function Operations – Logarithmic Functions – Sequences and Series – Matrices Hand-on lessons involving MATLAB projects Teacher notes

Unit Introduction Students research, explore, and discuss current applications of speech and audio processing

Lesson 1: The Sound of a Sine Wave Modeling sound as a sinusoidal function Concepts covered: – Continuous vs. Discrete Functions – Frequency of Sine Wave – Composite signals Connections to real-world applications: – Synthesis of digital speech and music

Lesson 1: The Sound of a Sine Wave Student MATLAB Project – Create discrete sine waves with given frequencies – Create composite signal of the sine waves – Plot graphs and play sounds of the sine waves – Analyze the effect of frequency on the graphs and the sounds of the sine functions Project Extensions – Play songs using sine waves – Synthesize vowel sounds with sine waves

Lesson 2: Frequency Analysis Use of Fourier Transformation to transform functions from time domain to frequency domain Concepts covered: – Modeling harmonic signals as a series of sinusoids – Sine wave decomposition – Fourier Transform – Euler’s Formula – Frequency spectrum Connections to real-world applications: – Speech processing and recognition

Lesson 2: Frequency Analysis Student MATLAB Project – Create a composite signal with the sum of harmonic sine waves – Plot graphs and play sounds of the sine waves – Compute the FFT of the composite signal – Plot and analyze the frequency spectrum

Lesson 3: Sound Effects Concepts covered: Connections to real-world applications: – Digital music effects and speech sound effects

Lesson 3: Sound Effects Student MATLAB Project

Unit Conclusion Student presentation and report or poster – Summarize and reflect on lessons – Ask research questions – Develop new ideas for applications of speech processing

References Ingle, Vinay K., and John G. Proakis. Digital signal processing using MATLAB. 2nd ed. Toronto, Ont.: Nelson, Oppenheim, Alan V., and Ronald W. Schafer. Discrete-time signal processing. 3rd ed. Upper Saddle River: Pearson, Weeks, Michael. Digital signal processing using MATLAB and wavelets. Hingham,Mass.: Infinity Science Press, Timeline of Speech Recognition.

AEGIS website: Lesson plans available for download ????? Contacts: – Becky Dowell, – Dr. Veton Këpuska, – Jacob Zurasky, AEGIS Project

Thank you! Questions?