Acoustics of Speech

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Acoustics of Speech

• Julia Hirschberg
• CS 4706

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Claim How things are said can be critical to
understanding

• I.e., Varying phrasing, prominence, pitch range,
  speaking rate, pitch contour, voice
  qualityconveys meaning
• What is our evidence? How do we prove?
• Observation
• Hypotheses
• Experimentation (perception, production)
• Speech analysis (independent variables)
• Correlation with dependent variable

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• What does our data look like?
• What tools do we have for analysis?

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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?

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• 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

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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

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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 Hz1sec/period_in_sec
• Horizontal axis of waveform
• Amplitude peak deviation of pressure from normal
  atmospheric pressure

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• 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 (Fouriers theorem)
• E.g. some speech sounds (mostly vowels) cat.wav

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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)

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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

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Vocal fold vibration
UCLA Phonetics Lab demo
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Places of articulation
http//www.chass.utoronto.ca/danhall/phonetics/sa
mmy.html
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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)

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• 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)

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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)

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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, )

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Sampling Errors

• Aliasing
• Signals frequency higher than half the sampling
  rate
• Solutions
• Increase the sampling rate
• Filter out frequencies above half the sampling
  rate (anti-aliasing filter)

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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)

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• But clipping occurs when input volume is greater
  than range representable in digitized waveform
• Increase the resolution
• Decrease the amplitude

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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

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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)

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• 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/)

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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 155550 1991
• operation record padding xxxxxxxxxxxx

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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

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Pitch Perception

• But do pitch trackers capture what humans
  perceive?
• Auditory systems 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

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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

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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
  sointensity 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

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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.

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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.columb
  ia.edu/julia/cs4706/cc_001_sadness_1669.04_August
  -second-.wav or record your own)