1 / 33

Acoustics of Speech

Acoustics of Speech. Julia Hirschberg CS 4706. Goal 1: Distinguishing One Phoneme from Another, Automatically. ASR: Did the caller say ‘ I want to fly to Newark ’ or ‘ I want to fly to New York ’? Forensic Linguistics: Did the accused say ‘ Kill him’ or ‘ Bill him’ ?

devona
Download Presentation

Acoustics of Speech

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Acoustics of Speech Julia Hirschberg CS 4706

  2. Goal 1: Distinguishing One Phoneme from Another, Automatically • ASR: Did the caller say ‘I want to fly to Newark’ or ‘I want to fly to New York’? • Forensic Linguistics: Did the accused say ‘Kill him’ or ‘Bill him’? • What evidence is there in the speech signal? • How accurately and reliably can we extract it?

  3. Goal 2: Determining How things are said is sometimes critical to understanding • Forensic Linguistics: ‘Kill him!’ or ‘Kill him?’ • Call Center: ‘That amount is incorrect.’ • What information do we need to extract from the speech signal? • What tools do we have to do this?

  4. Today and Next Class • Acoustic features to extract • Fundamental frequency (pitch) • Amplitude/energy (loudness) • Spectrum • Timing (pauses, rate) • Tools for extraction • Praat • Wavesurfer • Xwaves

  5. Sound Production • 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 • Plot sound as change in air pressure over time • 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 • Higher SNRs are better

  6. How ‘Loud’ are Common Sounds – How Much Pressure Generated? 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

  7. 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 = cycles per second or 1/period • E.g. 400Hz pitch = 1/.0025 (1 cycle has a period of .0025; 400 cycles complete in 1 sec) • Amplitude:peak deviation of pressure from normal atmospheric pressure

  8. Phase: timing of waveform relative to a reference point

  9. Complex Periodic Waves • Cyclic but composed of multiple 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)

  10. Some Sounds are Aperiodic • Waveforms with random or non-repeatingpatterns • 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)

  11. 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 component frequencies amplified by vocal track

  12. Vocal fold vibration [UCLA Phonetics Lab demo]

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

  14. 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)

  15. 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)

  16. 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)

  17. 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, …)

  18. 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)

  19. 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)

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

  21. 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

  22. 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)

  23. Errors to watch for: • Halving: shortest lag calculated is too long (underestimate pitch) • Doubling: shortest lag too short (overestimate pitch) • Microprosody effects (e.g. /v/)

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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….

  30. 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) • Bring a laptop and headphones to class if you have them

More Related