Transcript Perception and Production in L2

Perception and Production in L2 Acquisition
Lab 1 12 november 2008.ppt
Week 1: A Brief Introduction to Acoustic Phonetics
Tom Lentz
(slides Ivana Brasileiro)
Acoustic Phonetics
• Physics of the speech signal
• Relationship between activity in the
speaker's vocal tract and the resulting
sounds
• Contrast: articulatory phonetics
What we will do…
• Key concepts: frequency, formants, and
acoustic cues
• Measuring speech sound
• Vowels and formants
• (Acoustic cues: VOT)
• (Problems in Language Acquisition)
Frequency
Cycles of vibration per second
Measured in Hertz (Hz)
E.g. 100 Hz = 100 repetition per second
Frequency
Figure 1: Two periodic signals with frequencies of 200Hz and 400Hz
Sine Waves vs. Complex Sound Waves
• Sine waves: sounds formed by one
frequency only
• Complex sound waves: all sounds which are
not sine waves
• All complex sound waves can be described
on the basis of the sine waves
Sine Waves vs. Complex Sound Waves
a
d
b
c
Figure 2: three sine waves (left) and resulting complex sound wave (right)
Question
• If in figure 2, (a) has frequency A; (b) has
frequency B and (c) has frequency C, what
is the frequency of picture (d)?
• Answer: A
Speech Sounds
• Speech sounds are always complex waves
• Two sources of sounds
– Vocal folds
– Oral cavity
• Sounds produced with the vocal folds are
periodic: usually perceived as voiced
• Sounds produced with the oral cavity are
aperiodic: usually perceived as voiceless
Speech Sounds
Periodic
Aperiodic
Measuring Sounds
• Oscillogram
• Spectrum
• Spectrogram
Oscillogram
• Amplitude x time
Spectrum
• Shows all frequencies present in the
signal at a given point in time
Spectrogram
• Combines properties of oscillogram and spectrum
• Measure three dimensions: time, frequency, and
amplitude
PRAAT practice
Task: find vocal fold vibration frequency
• Record your voice: which phonemes ?
• Analyse: how?
Formants (1)
• The shape of the vocal tract increases some
frequencies and decreases others
• The increased frequencies can be seen in the
spectrum as peaks; and in spectrograms as
darker spots
• These frequency peaks are the formants
Formants (2)
• Formants are commonly used to describe
vowels
• The first 3 formants (F1, F2 and F3) are
important for the vowel quality
• Other formants (F4 and F5) are important
for the naturalness of the speech sound
Vowels Acoustic Space
• Vowels are usually plotted in a F1 x F2
graph, since they play a prominent role in
the quality of the vowel
• F1 in the y-axis and F2 in the x-axis
• This graphic represents the acoustic space
of vowels, the so called vowel triangle (see
handout monday)
Articulatory correlates
• F1 correlates with vowel height
– higher F1, lower vowel
• F2 correlates with degree of frontness
– higher F2, ‘fronter’ vowel
Dutch Vowels
Acoustic Cues
• Refers to any piece of signal that has been
found by experiment to have an effect on
percept (Lieberman 1996)
• Examples of cues are: VOT, bursts,
transitions, duration and formants
Voice Onset Time (VOT)
• Refers to the time of voicing in relation to
the consonant articulation / release burst
Acoustic Cues
• No one-to-one relationship between ‘cues’ and a
single percept (see handout)
• Voicing contrast in Dutch has been shown to have
about 6 acoustic correlates
• Cues differ in how important they are
• “Cue reliance” refers to how strongly listeners rely
on a specific cue to identify a contrast
• “Cue weighting” refers to how much each cue
counts
Problems in Language Acquisition
• Segmentation problem
– Coarticulation
– Cue ambiguity
• Mapping problem
Coarticulation
• Information about more than one sound is
often encoded in the same portion of the
acoustic signal
Cue ambiguity
• The interpretation of acoustic cues depends
on their position in the signal (handout
Kager)
• Example: VOT in English ‘pin’ ‘spin’ ‘bin’
• ‘pin’: aspirated, long lag VOT (30-35ms)
• ‘spin’: plain, voiceless unaspirated, short
lag VOT
• ‘bin’: voiced, short lag VOT or prevoiced
Mapping problem
• How do phonetic categories relate to
phonological categories?
• Allophonic variation
• Contextual variation
• Normalization