X-axis: time (in s). Y-axis: perceived pitch (in semitones, ST); dotted grid lines are 2 ST apart.
The vertical dotted lines show segmentation boundaries.
Click on the picture to see a full size, high resolution version in GIF file format.
The large format adds the calibration of X and Y axes (in ST, relative to 1 Hz).
Rich format adds intensity (thin green line) and F0 (thick blue line) on a ST scale, for validating the results.
This prosogram uses automatic segmentation based on intensity of band-pass filtered signal.
Since the early days of intonation research, automatic transcription of the intonation
in speech corpora has been on the wish list of many a researcher in
phonetics, linguistics, and discourse analysis.
Most phoneticians use the fundamental frequency (F0) to represent pitch contours in speech. F0 is an acoustic parameter;
it provides useful information about the acoustic properties of the speech signal.
But it certainly is not the most accurate representation of the
intonation contour as it is perceived by human listeners.
In the seventies pitch contour stylization was introduced as a way to simplify
the F0 curve to those aspects which are relevant for speech communication.
The approach originates from work by J. 't Hart, R. Collier, and A. Cohen at IPO,
Eindhoven, and was further improved by D. Hermes in the '80 and '90.
Other types of stylization have been proposed, such as the
by D. Hirst, R. Espesser from Aix-en-Provence.
However, most of these stylization approaches are based on statistical or mathematical
properties of the F0 data and mostly ignore the facts of pitch perception.
It is well known that the auditory perception of pitch variations depends
on many factors other than F0 variation itself.
In 1995 a stylization based on the simulation of tonal perception
was proposed by Ch. d'Alessandro & P. Mertens
(Mertens & d'Alessandro, 1995,
d'Alessandro & Mertens, 1995).
The purpose of this stylization is to provide a curve which approximates the
auditory image in the listener's mind.
This tonal perception model was validated in listening experiments using stimuli
resynthesized using the stylized contour
(Mertens et al, 1997).
This same approach may be used to obtain a transcription
This requires a segmentation of the speech signal into syllable-sized units,
that are motivated by phonetic, acoustic or perceptual properties.
The prosogram may be used in conjunction with various types of segmentation:
an automatic segmentation into local peaks in the intensity of the band-pass filtered speech signal;
a (manual) segmentation into vowels, stored in an phonetic alignment file (Praat's TextGrid files);
a (manual) segmentation into syllables;
a (manual) segmentation into syllable rimes;
any segmentation provided by an external program.
The stylization is applied to the F0 curve of those segmented units, which are approximations of the syllabic nuclei.
Obtain a segmentation into units of the types indicated above.
Select the relevant units (e.g. vowels, syllables).
Select the voiced portion of these units, that has sufficient intensity/loudness
(using difference thresholds relative to the local peak).
Stylize the F0 of the selected time intervals.
Determine pitch range used in speech fragment.
Plot stylized pitch and some annotation tiers (text, phonetic transcription).
Use a musical (semitone) scale and add calibration lines
at every 2 ST for easy interpretation of pitch intervals.
The system is implemented as a Praat script.
Praat is a tool for acoustic and phonetic research,
written by Paul Boersma and David Weenink, of the Institute of Phonetic Sciences in Amsterdam.
The choice of Praat is motivated by the fact that
it is powerful, user-friendly, programmable, freely available,
running on many platforms, and actively maintained.
automatic segmentation The automatic segmentation mode of Prosogram does not require a preliminary segmentation
into sounds or syllables.
This kind of segmentation is based on acoustic parameters
or on an estimation of perceived attributes of the signal.
The current version of the prosogram uses a segmentation based on loudness,
computed from the cochleagram.
It does not identify the underlying speech sounds.
manual segmentation It can be obtained using Praat and will be stored in a "TextGrid" file.
semi-automatic segmentation Alternatively the segmentation can be obtained semi-automatically,
using automatic alignement with some phonetic transcription of the speech signal.
The alignment will be based either on automatic speech recognition (ASR), or on
The phonetic transcription can be obtained either manually, or using
grapheme-to-phoneme conversion and natural language processing (NLP).
Jean-Philippe Goldman provides a phonetic alignment system
Easy Align, based on ASR and grapheme-to-phoneme conversion.
A small corpus of spoken French was processed
to illustrate the results obtained with the transcription tool.
The corpus consists of about 4 minutes of an interview between Fayard and
Benoîte Groult broadcasted on Radio de la Suisse Romande.
Some F0 variations are clearly perceived as rises or falls; others go unnoticed
unless after repeated listening; still others are simply not perceived at all.
Indeed, tonal perception depends upon several factors.
The auditory threshold for pitch variation, known as the glissando
threshold G. It depends on the amplitude (extent) and the duration of the
F0 variation. Since the work of J. 't Hart, it is usually expressed in ST/s (semitones per second).
In hearing experiments using short stimuli, either pure tones or speech-like signals,
with repeated presentations,
a glissando threshold G = 0.16/T^2 was measured.
Changes in the spectral properties of the signal tend to function as boundaries
(House, 1990), breaking up a voiced continuum into a sequence of syllabic nuclei.
Changes in signal amplitude tend to function as boundaries.
The presence of a pause following the F0 variation lowers the threshold for the
perception of that variation (House, 1995).
A change in slope is perceived provided it is sufficiently large.
This is called the differential glissando threshold.
Our approach to stylization takes into account
the segmentation into syllabic (or vocalic) nuclei, due to spectral and amplitude changes,
the glissando threshold,
the differential glissando threshold.
The two latter thresholds are model parameters, which can be adjusted.
The stylization will show the effect on the estimated perceived pitch contour.
This is shown in the next sample, which compares the F0 curve and two
stylization variants: the first with G=0.16/T^2,
the second with G=0.32/T^2, i.e. a glissando threshold twice as high.
The (intravocalic) pitch movements found on "les", "chefs" and "gieux", in the case of G=0.16/T^2,
no longer appear in the stylization with G=0.32/T^2.
In normal conversation, utterances are heard only once.
Given the continuous flow of speech, the listener has no time to reflect on the
auditory properties of the signal.
How then should the glissando parameter be chosen in order to obtain
a correct representation of pitch perception in continuous speech ?
The intonation of Groult corpus has been transcribed by ear by two
This auditory transcription was compared with various stylizations using
different settings of the stylization parameters.
The setting with G=0.32/T^2 better matches the performance of the human transcribers.
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