Acoustics of Speech Julia Hirschberg CS 4706 1/2/2019

Claim: How things are said can be critical to understanding I.e., Varying phrasing, prominence, pitch range, speaking rate, pitch contour, voice quality…conveys meaning What is our evidence? How do we prove? Observation Hypotheses Experimentation (perception, production) Speech analysis (independent variables) Correlation with dependent variable 1/2/2019

What does our data look like? What tools do we have for analysis? 1/2/2019

What is sound? Pressure fluctuations in the air caused by a musical instrument, a car horn, a voice Cause eardrum to move Auditory system translates into neural impulses Brain interprets as sound Can we tell one sound from another? Can we distinguish one particular sound in ‘noise’? 1/2/2019

From a speech-centric point of view, when sound is not produced by the human voice, we may term it noise Ratio of speech-generated sound to other simultaneous sound: signal-to-noise ratio 1/2/2019

How ‘Loud’ are Common Sounds? Event Pressure (Pa) Db Absolute 20 0 Whisper 200 20 Quiet office 2K 40 Conversation 20K 60 Bus 200K 80 Subway 2M 100 Thunder 20M 120 *DAMAGE* 200M 140 1/2/2019

Some Sounds are Periodic Simple Periodic Waves (sine waves) defined by Frequency: how often does pattern repeat per time unit Cycle: one repetition Period: duration of cycle Frequency=# cycles per time unit, e.g. Frequency in Hz=1sec/period_in_sec Horizontal axis of waveform Amplitude: peak deviation of pressure from normal atmospheric pressure 1/2/2019

Phase: timing of waveform relative to a reference point Complex periodic waves Cyclic but composed of two or more sine waves Fundamental frequency (F0): rate at which largest pattern repeats (also GCD of component freqs) Components not always easily identifiable: power spectrum graphs amplitude vs. frequency Any complex waveform can be analyzed into a set of sine waves with their own frequencies, amplitudes, and phases (Fourier’s theorem) E.g. some speech sounds (mostly vowels) cat.wav 1/2/2019

Some Sounds are Aperiodic Waveforms with random or non-repeating patterns Random aperiodic waveforms: white noise Flat spectrum: equal amplitude for all frequency components Transients: sudden bursts of pressure (clicks, pops, door slams) Waveform shows a single impulse (click.wav) Fourier analysis shows a flat spectrum Some speech sounds, e.g. many consonants (e.g. cat.wav) 1/2/2019

Speech Production Voiced and voiceless sounds Vocal fold vibration filtered by the Vocal tract produces complex periodic waveform Cycles per sec of lowest frequency component of signal = fundamental frequency (F0) Fourier analysis yields power spectrum with component frequencies and amplitudes F0 is first (lowest frequency) peak Harmonics are resonances of vocal track, multiples of F0 1/2/2019

Vocal fold vibration [UCLA Phonetics Lab demo] 1/2/2019

Places of articulation alveolar post-alveolar/palatal dental velar uvular labial pharyngeal laryngeal/glottal http://www.chass.utoronto.ca/~danhall/phonetics/sammy.html 1/2/2019

How do we capture speech for analysis? Recording conditions A quiet office, a sound booth, an anachoic chamber Microphones Analog devices (e.g. tape recorders) store and analyze continuous air pressure variations (speech) as a continuous signal Digital devices (e.g. computers,DAT) first convert continuous signals into discrete signals (A-to-D conversion) 1/2/2019

Conversion programs, e.g. sox Storage File format: .wav, .aiff, .ds, .au, .sph,… Conversion programs, e.g. sox Storage Function of how much information we store about speech in digitization Higher quality, closer to original More space (1000s of hours of speech take up a lot of space) 1/2/2019

Sampling Sampling rate: how often do we need to sample? At least 2 samples per cycle to capture periodicity of a waveform component at a given frequency 100 Hz waveform needs 200 samples per sec Nyquist frequency: highest-frequency component captured with a given sampling rate (half the sampling rate) 1/2/2019

Sampling/storage tradeoff Human hearing: ~20K top frequency Do we really need to store 40K samples per second of speech? Telephone speech: 300-4K Hz (8K sampling) But some speech sounds (e.g. fricatives, /f/, /s/, /p/, /t/, /d/) have energy above 4K! Peter/teeter/Dieter 44k (CD quality audio) vs.16-22K (usually good enough to study pitch, amplitude, duration, …) 1/2/2019

Sampling Errors Aliasing: Signal’s frequency higher than half the sampling rate Solutions: Increase the sampling rate Filter out frequencies above half the sampling rate (anti-aliasing filter) 1/2/2019

Quantization Measuring the amplitude at sampling points: what resolution to choose? Integer representation 8, 12 or 16 bits per sample Noise due to quantization steps avoided by higher resolution -- but requires more storage How many different amplitude levels do we need to distinguish? Choice depends on data and application (44K 16bit stereo requires ~10Mb storage) 1/2/2019

But clipping occurs when input volume is greater than range representable in digitized waveform Increase the resolution Decrease the amplitude 1/2/2019

What can we do if our data is ‘noisy’? Acoustic filters block out certain frequencies of sounds Low-pass filter blocks high frequency components of a waveform High-pass filter blocks low frequencies Reject band (what to block) vs. pass band (what to let through) But if frequencies of two sounds overlap….source separation 1/2/2019

How can we capture pitch contours, pitch range? What is the pitch contour of this utterance? Is the pitch range of X greater than that of Y? Pitch tracking: Estimate F0 over time as fn of vocal fold vibration A periodic waveform is correlated with itself One period looks much like another (cat.wav) Find the period by finding the ‘lag’ (offset) between two windows on the signal for which the correlation of the windows is highest Lag duration (T) is 1 period of waveform Inverse is F0 (1/T) 1/2/2019

Halving: shortest lag calculated is too long (underestimate pitch) Errors to watch for: Halving: shortest lag calculated is too long (underestimate pitch) Doubling: shortest lag too short (overestimate pitch) Microprosody errors (e.g. /v/) 1/2/2019

Sample Analysis File: Pitch Track Header version 1 type_code 4 frequency 12000.000000 samples 160768 start_time 0.000000 end_time 13.397333 bandwidth 6000.000000 dimensions 1 maximum 9660.000000 minimum -17384.000000 time Sat Nov 2 15:55:50 1991 operation record: padding xxxxxxxxxxxx 1/2/2019

Sample Analysis File: Pitch Track Data (F0 Pvoicing Energy A/C Score) 147.896 1 2154.07 0.902643 140.894 1 1544.93 0.967008 138.05 1 1080.55 0.92588 130.399 1 745.262 0.595265 0 0 567.153 0.504029 0 0 638.037 0.222939 0 0 670.936 0.370024 0 0 790.751 0.357141 141.215 1 1281.1 0.904345 1/2/2019

Pitch Perception But do pitch trackers capture what humans perceive? Auditory system’s perception of pitch is non-linear Sounds at lower frequencies with same difference in absolute frequency sound more different than those at higher frequencies (male vs. female speech) Bark scale (Zwicker) and other models of perceived difference 1/2/2019

How do we capture loudness/intensity? Is one utterance louder than another? Energy closely correlated experimentally with perceived loudness For each window, square the amplitude values of the samples, take their mean, and take the root of that mean (RMS energy) What size window? Longer windows produce smoother amplitude traces but miss sudden acoustic events 1/2/2019

Perception of Loudness But the relation is non-linear: sones or decibels (dB) Differences in soft sounds more salient than loud Intensity proportional to square of amplitude so…intensity of sound with pressure x vs. reference sound with pressure r = x2/r2 bel: base 10 log of ratio decibel: 10 bels dB = 10log10 (x2/r2) Absolute (20 Pa, lowest audible pressure fluctuation of 1000 Hz tone), typical threshold level for tone at frequency 1/2/2019

How do we capture…. For utterances X and Y Pitch contour: Same or different? Pitch range: Is X larger than Y? Duration: Is utterance X longer than utterance Y? Speaker rate: Is the speaker of X speaking faster than the speaker of Y? Voice quality…. 1/2/2019

Next Class Tools for the Masses: Read the Praat tutorial Download Praat from the course syllabus page and play with a speech file (e.g. http://www.cs.columbia.edu/~julia/cs4706/cc_001_sadness_1669.04_August-second-.wav or record your own) 1/2/2019