Time Frequency Function

When considering how the frequency changes in a given time domain

we often judge by looking at (or hopefully calculating) the

frequency change but there are times that utterances are being

prolonged (mostly duration is a function of tone i.e. our tone

will alter duration; With that being said most of my calculations

do not consider duration or time as an independent variable);

This increase in producing the utterance does have a pragmatic

effect and thus it is worthwhile to consider it in transcribing

prosody.

ToBI is a system of transcribing prosodic features of speech.

ToBI is easy to learn, easy to share, and easy to use. these 3

features have made ToBI a widely used system for transcribing

prosody. But ToBI has some downfalls too. Since it is based on

the phonological level of the speech it does not contain

phonetic features of speech. I have some ideas to implement

phonetic features of prosody in ToBI and on the top of that

adding frequency change in the ToBI system.

I, therefore, decided to design a formula based on frequency time

change. The formula is:

Z is the time frequency function on the scale from 1.0 to 4.0 where Z from 1.0 to 2.0 is flagged as low 2.0 to 3.0 flagged as medium and lastly 3.0 to 4.0 flagged as high. F is the frequency in Hertz and it is scaled from 75 to 300 . T is short for Time on FFT frequency display. Both F and T are indicators of the change in the pitch track and hence they are calculated as: F2 - F1 and T2 - T1. T is always positive bud F can sometimes be negative so I used Absolute value in the formula to avoid F being zero.

I have written a Python code that plots the distribution of the

outputs across Z axis. X axis is T and Y axis is F.

The Python code is:

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np

# introduce bases
freq_min, freq_max, freq_spacing = 75., 300., 5.
freqs = np.arange(freq_min, freq_max + freq_spacing, 

freq_spacing)

t_min, t_max, t_spacing = 0.1, 5., 0.1
ts = np.arange(t_min, t_max + t_spacing, t_spacing)

# do calculation
F, T = np.meshgrid(freqs, ts)
Z =  F  /  T * 2
Z2 = np.log10(Z)
print (Z2)
# display result
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(F, T, Z2,
    rstride=1, cstride=1, cmap=cm.RdPu, antialiased=True, 

linewidth=0.2)
ax.view_init(elev=35, azim=160)
ax.dist = 10
plt.show()

The proposed symbols for transcribing this scale in ToBI is using

commas, semicolons, and colons.

a) because it’s more intuitive

b) because it is easily read.

[comma] for low change, [semicolon] for medium change,

and [colon] for high change. examples are:

(a) L+H:% (Indicaating high frequency change in the given

time domain)

(b) L+H;* (Indicating medium frequency change in the given

time domain)

(c) H,* (Indicating low frequency change in the given time

domain)

Consider the following example:

This is a recording from Kurmanji Kurdish.

The values for F and T are:

f1 : 187.61 Hz
t1 : 1.55 
f2 : 104.12 Hz
t2 : 1.81

T : .26
F : 83.49

Calculating the above data we will get:

>>> import numpy as np
>>> F = 83.49
>>> T = 0.26
>>> Z =  F  /  T * 2
>>> Z2 = np.log10(Z)
>>> print (Z2)
2.80769110875

2.80 in our Time Frequency Function (TFF) is considered

Medium change so we transcribe it as: H+L;%

Edit Feb 16: On the second thought I observed that there are numerous problems with the ToBI and the AM so I’m thinking of changing the framework. I’ll post later if I had another idea.