# Manner of Articulation¶

Mark Hasegawa-Johnson January 31, 2018

3. The two-bit definition of manner: glides, nasals, fricatives, and stops
4. sonorant
5. continuant

The key functions from lectures 1 and 2 (https://courses.engr.illinois.edu/ece590sip/sp2018/spectrograms1_wideband_narrowband.html) are now available in a library that you can download: https://courses.engr.illinois.edu/ece590sip/sp2018/sgram.py . The following code might or might not work, depending on your system's security settings. If it does work, it should load sgram.py into your environment, and then show the docstring for the sgram.sgram function.

In [3]:
import urllib.request as request
import io
sgram_url = 'https://courses.engr.illinois.edu/ece590sip/sp2018/sgram.py'
with open('sgram.py','wb') as f:
f.write(sgram_dot_py_whole_program_as_one_long_string)
import sgram
sgram.sgram?


Today I want to talk about manner of articulation of consonants. So let's download example audio files for all of the English consonants from wikipedia.

WARNING: the pathnames listed below are the pathnames on wikipedia as of Jan 30, 2018. They might change in the future. If one of the pathnames changes in the future, you will get an HTTPError, and the "except request.HTTPError" clause in the code below will print out the URL that is missing. If that happens, you should go to https://en.wikipedia.org/wiki/International_Phonetic_Alphabet, find the phoneme that is missing, find the javascript that plays it, click the "source" link under that javascript, find the OGG filename on the page that opens up, right-click on the filename and choose "copy link address" to get the complete URL, then update just the changed part in the pathname dictionary below.

In [4]:
import soundfile as sf
import io
consonant_pathnames = {
'w' : 'f/f2/Voiced_labio-velar_approximant',
'r' : '3/33/Postalveolar_approximant',
'l' : 'b/bc/Alveolar_lateral_approximant',
'j' : 'e/e8/Palatal_approximant',
'm' : 'a/a9/Bilabial_nasal',
'n' : '2/29/Alveolar_nasal',
'ŋ' : '3/39/Velar_nasal',
'f' : '3/33/Voiceless_labiodental_fricative',
'v' : '8/85/Voiced_labiodental_fricative',
'θ' : '8/80/Voiceless_dental_fricative',
'ð' : '6/6a/Voiced_dental_fricative',
's' : 'a/ac/Voiceless_alveolar_sibilant',
'z' : 'c/c0/Voiced_alveolar_sibilant',
'ʃ' : 'c/cc/Voiceless_palato-alveolar_sibilant',
'ʒ' : '3/30/Voiced_palato-alveolar_sibilant',
'p' : '5/51/Voiceless_bilabial_plosive',
'b' : '2/2c/Voiced_bilabial_plosive',
't' : '0/02/Voiceless_alveolar_plosive',
'd' : '0/01/Voiced_alveolar_plosive',
'k' : 'e/e3/Voiceless_velar_plosive',
'g' : '1/12/Voiced_velar_plosive_02'
}

consonant_waves = {}

for c_ipa,c_pathname in consonant_pathnames.items():
try:
req = request.urlopen(c_url)
except request.HTTPError:
else:
c_filename = c_pathname[5:] + '.wav'
sf.write(c_filename,c_wav,c_fs)
consonant_waves[c_ipa] = c_wav


Donwnloaded these phones: dict_keys(['w', 'r', 'l', 'j', 'm', 'n', 'ŋ', 'f', 'v', 'θ', 'ð', 's', 'z', 'ʃ', 'ʒ', 'p', 'b', 't', 'd', 'k', 'g'])


Let's just check to make sure that we have the waveforms, and we can compute a spectrogram:

In [5]:
import matplotlib.pyplot as plt
%matplotlib inline
(x_sgram,x_extent)=sgram.sgram(consonant_waves[ 'ŋ'], int(0.001*c_fs), int(0.006*c_fs), 1024, c_fs, 5000)
plt.figure(figsize=(14,4))
plt.imshow(x_sgram,origin='lower',extent=x_extent,aspect='auto')

Out[5]:
<matplotlib.image.AxesImage at 0x13cb606e2e8>

## 3. A two-bit definition of manner¶

IPA has an overly complicated definition of "manner of articulation," which leads people to the mistaken idea that speech is more complicated than non-speech. It isn't, really. There are only four manner classes that differ in an important way. They are:

1. Vowels, Glides, and Liquids are [+sonorant, +continuant]
2. Nasals are [+sonorant, -continuant]
3. Fricatives are [-sonorant, +continuant]
4. Stops, Clicks, and Silences are [-sonorant, -continuant]

Here's that same list in the form of a table:

[+sonorant] [-sonorant]
[+continuant] Vowel, Glide Fricative
[-continuant] Nasal Stop, Silence

## 4. sonorant¶

The feature [+sonorant] means that there is full-strength voicing. Full-strength voicing happens when there is no buildup of pressure inside the vocal tract---all of the pressure drop is from below the glottis (lung pressure) to above the glottis (room pressure).

The alternative is a [-sonorant] consonant, also known as "obstruent."

An obstruent consonant can be voiced, but its voicing is always weaker than the corresponding sonorant. Voicing for the sonorant consonant fills up the entire frequency band from 0Hz to 800Hz, and continues full strength without dying away at any particular time. Voicing for the obstruent consonant is limited to only the very lowest frequencies, maybe just 0Hz to 200Hz, and it dies away to almost nothing in the middle of the consonant.

### 4a. Example 1: /j/ vs. /ʒ/¶

First, let's consider the consonants /j/ (a palatal glide, as in "yacht") and /ʒ/ (a palatal voiced fricative, as in "azure"). Both are made with the tongue body raised up toward the hard palate (so we say that they have "palatal" place of articulation). Neither has the airflow cut off entirely (i.e., both are [+continuant]), but /ʒ/ has a tighter constriction, so the air pressure between the glottis and the tongue body constriction is raised. This raised air pressure, inside the mouth, causes voicing to have a reduced amplitude. Even more visible in the spectrogram: (1) voicing for /ʒ/ only covers the frequency band up to about 300Hz, whereas for /j/ you can see the voiced energy up to about 800Hz, (2) for /ʒ/, you can also see frication at high frequencies around 2000Hz, whereas for /j/, the energy at around 2000Hz is still periodic voiced energy.

In [5]:
(j_sgram,j_extent)=sgram.sgram(consonant_waves[ 'j'], int(0.001*c_fs), int(0.006*c_fs), 1024, c_fs, 5000)
(zh_sgram,zh_extent)=sgram.sgram(consonant_waves['ʒ'], int(0.001*c_fs), int(0.006*c_fs), 1024, c_fs, 5000)
plt.figure(figsize=(14,9))
plt.subplot(211)
plt.imshow(j_sgram,origin='lower',extent=j_extent,aspect='auto')
plt.title('Spectrogram of /ja/, /aja/')
plt.subplot(212)
plt.imshow(zh_sgram,origin='lower',extent=zh_extent,aspect='auto')
plt.title('Spectrogram of /ʒa/, /aʒa/')

Out[5]:
<matplotlib.text.Text at 0x18a00282a58>

### 4b. Example: /w/ vs. /v/¶

For another example, consider the labial glide /w/, and the labial voiced fricative /v/. These have almost the same place of articulation (or as close as you can get in English, anyway). They are both voiced, and both [+continuant]. But /v/ has a tighter constriction, so pressure inside the vocal tract is raised, therefore it is [-sonorant]. --- Notice from the images below that, unlike /ʒ/, the consonant /v/ actually has very little frication energy. The main difference between /w/ and /v/ is not the frication energy, it's the bandwidth and stability of voicing --- /w/ has stable voicing with 800Hz bandwidth all the way through the closure, while the first formant of /v/ has a bandwidth of only about 300Hz, and its energy decreases toward the last half of the consonant closure.

In all fairness, this is one of the hardest comparisons on the worksheet today. There is one auxiliary cue that's not just about the feature [sonorant], it's actually about the difference between bilabial articulation (/w/) versus labiodental articulation (/v/). Notice that, in the vowel /a/, there are two formants at about 900Hz and 1100Hz. For the /w/, both of those formants drop, to about 200Hz and 500Hz. For the /v/, only the first formant drops (to about 200Hz); the second formant stays high, above 1000Hz. That's the difference between bilabial versus labiodental articulation; it has nothing at all to do with the feature [sonorant].

In [7]:
(w_sgram,w_extent)=sgram.sgram(consonant_waves[ 'w'], int(0.001*c_fs), int(0.006*c_fs), 1024, c_fs, 5000)
(v_sgram,v_extent)=sgram.sgram(consonant_waves['v'], int(0.001*c_fs), int(0.006*c_fs), 1024, c_fs, 5000)
plt.figure(figsize=(14,9))
plt.subplot(211)
plt.imshow(w_sgram,origin='lower',extent=w_extent,aspect='auto')
plt.title('Spectrogram of /wa/, /awa/')
plt.subplot(212)
plt.imshow(v_sgram,origin='lower',extent=v_extent,aspect='auto')
plt.title('Spectrogram of /ava/')

Out[7]:
<matplotlib.text.Text at 0x18a00b61320>