The Phonetic Analysis of Speech Corpora - Phonetik und ...

The Phonetic Analysis of Speech Corpora
Jonathan Harrington
Institute of Phonetics and Speech Processing
Ludwig-Maximilians University of Munich
Germany email: jmh@phonetik.uni-muenchen.de Wiley-Blackwell
Contents
Relationship between International and Machine Readable Phonetic Alphabet
(Australian English)
Relationship between International and Machine Readable Phonetic Alphabet
(German)
Downloadable speech databases used in this book
Preface
Notes of downloading software Chapter 1 Using speech corpora in phonetics research
1.0 The place of corpora in the phonetic analysis of speech
1.1 Existing speech corpora for phonetic analysis
1.2 Designing your own corpus
1.2.1 Speakers
1.2.2 Materials
1.2.3 Some further issues in experimental design
1.2.4 Speaking style
1.2.5 Recording setup
1.2.6 Annotation
1.2.7 Some conventions for naming files
1.3 Summary and structure of the book Chapter 2 Some tools for building and querying labelling speech databases
2.0 Overview
2.1 Getting started with existing speech databases
2.2 Interface between Praat and Emu
2.3 Interface to R
2.4 Creating a new speech database: from Praat to Emu to R
2.5 A first look at the template file
2.6 Summary
2.7 Questions Chapter 3 Applying routines for speech signal processing
3.0 Introduction
3.1 Calculating, displaying, and correcting formants
3.2 Reading the formants into R
3.3 Summary
3.4 Questions
3.5 Answers Chapter 4 Querying annotation structures
4.1 The Emu Query Tool, segment tiers and event tiers
4.2 Extending the range of queries: annotations from the same tier
4.3 Inter-tier links and queries
4.4 Entering structured annotations with Emu
4.5 Conversion of a structured annotation to a Praat TextGrid
4.6 Graphical user interface to the Emu query language
4.7 Re-querying segment lists
4.8 Building annotation structures semi-automatically with Emu-Tcl
4.9 Branching paths
4.10 Summary
4.11 Questions
4.12 Answers Chapter 5 An introduction to speech data analysis in R: a study of an EMA
database
5.1 EMA recordings and the ema5 database
5.2 Handling segment lists and vectors in Emu-R
5.3 An analysis of voice onset time
5.4 Inter-gestural coordination and ensemble plots
5.4.1 Extracting trackdata objects
5.4.2 Movement plots from single segments
5.4.3 Ensemble plots
5.5 Intragestural analysis
5.5.1 Manipulation of trackdata objects
5.5.2 Differencing and velocity
5.5.3 Critically damped movement, magnitude, and peak velocity
5.6 Summary
5.7 Questions
5.8 Answers Chapter 6 Analysis of formants and formant transitions
6.1 Vowel ellipses in the F2 x F1 plane
6.2 Outliers
6.3 Vowel targets
6.4 Vowel normalisation
6.5 Euclidean distances
6.5.1 Vowel space expansion
6.5.2 Relative distance between vowel categories
6.6 Vowel undershoot and formant smoothing
6.7 F2 locus, place of articulation and variability
6.8 Questions
6.9 Answers Chapter 7 Electropalatography
7.1 Palatography and electropalatography
7.2 An overview of electropalatography in Emu-R
7.3 EPG data reduced objects
7.3.1 Contact profiles
7.3.2 Contact distribution indices
7.4 Analysis of EPG data
7.4.1 Consonant overlap
7.4.2 VC coarticulation in German dorsal fricatives
7.5 Summary
7.6 Questions
7.7 Answers Chapter 8 Spectral analysis.
8.1 Background to spectral analysis
8.1.1 The sinusoid
8.1.2 Fourier analysis and Fourier synthesis
8.1.3 Amplitude spectrum
8.1.4 Sampling frequency
8.1.5 dB-Spectrum
8.1.6 Hamming and Hann(ing) windows
8.1.7 Time and frequency resolution
8.1.8 Preemphasis
8.1.9 Handling spectral data in Emu-R
8.2 Spectral average, sum, ratio, difference, slope
8.3 Spectral moments
8.4 The discrete cosine transformation
8.4.1 Calculating DCT-coefficients in EMU-R
8.4.2 DCT-coefficients of a spectrum
8.4.3 DCT-coefficients and trajectory shape
8.4.4 Mel- and Bark-scaled DCT (cepstral) coefficients
8.5 Questions
8.6 Answers Chapter 9 Classification
9.1 Probability and Bayes theorem
9.2 Classification: continuous data
9.2.1 The binomial and normal distributions
9.3 Calculating conditional probabilities
9.4 Calculating posterior probabilities
9.5 Two-parameters: the bivariate normal distribution and ellipses
9.6 Classification in two dimensions
9.7 Classifications in higher dimensional spaces
9.8 Classifications in time
9.8.1 Parameterising dynamic spectral information
9.9 Support vector machines
9.10 Summary
9.11 Questions
9.12 Answers
References
Relationship between Machine Readable (MRPA) and International Phonetic
Alphabet (IPA) for Australian English. MRPA IPA Example
Tense vowels
i: i: heed
u: ?: who'd
o: ?: hoard
a: ?: hard
@: ?: heard Lax vowels
I ? hid
U ? hood
E ? head
O ? hod
V ? bud
A æ had Diphthongs
I@ ?? here
E@ e? there
U@ ?? tour
ei æ? hay
ai ?? high
au æ? how
oi ?? boy
ou ?? hoe Schwa
@ ? the Consonants
p p pie
b b buy
t t tie
d d die
k k cut
g g go
tS ? church
dZ ? judge
H h (Aspiration/stop release)
m m my
n n no
N ? sing f f fan
v v van
T ? think
D ð the
s s see
z z zoo
S ? shoe
Z ? beige
h h he
r ? road
w w we
l l long
j j yes Relationship between Machine Readable (MRPA) and International Phonetic
Alphabet (IPA) for German. The MRPA for German is in accordance with SAMPA
(Wells, 1997), the speech assessment methods phonetic alphabet. MRPA IPA Example Tense vowels and diphthongs
2: ø: Söhne
2:6 ø? stört
a: a: Strafe, Lahm
a:6 a:? Haar
e: e: geht
E: ?: Mädchen
E:6 ?:? fährt
e:6 e:? werden
i: i: Liebe
i:6 i:? Bier
o: o: Sohn
o:6 o:? vor
u: u: tun
u:6 u:? Uhr
y: y: kühl
y:6 y:? natürlich
aI a? mein
aU a? Haus
OY ?Y Beute Lax vowels and diphthongs U ? Mund
9 ? zwölf
a a nass
a6 a? Mark
E ? Mensch
E6 ?? Lärm
I ? finden
I6 ?? wirklich
O ? kommt
O6 ?? dort
U6 ?? durch
Y Y Glück
Y6 Y? würde
6 ? Vater
Consonants p p Panne
b b Baum
t t Tanne
d d Daumen
k k kahl
g g Gaumen
pf pf Pfeffer
ts ? Zahn
tS ? Cello
dZ ? Job
Q ? (Glottal stop)
h h (Aspiration)
m m Miene
n n nehmen
N ? lang f f friedlich
v v weg
s s lassen
z z lesen
S ? schauen
Z ? Genie
C ç riechen
x x Buch, lachen
h h hoch r r, ? Regen
l l lang
j j jemand
Downloadable speech databases used in this book |Database |Description |
|name | |
|gerplosive|Isolated words in carrier sentence |
|s | |
stops |Isolated words in carrier sentence |German |470 |3M,4F |Audio,
formants |Phonetic |unpublished | |timetable |Timetable enquiries |German
|5 |1M |Audio |Phonetic |As kielread | | Preface
In undergraduate courses that include phonetics, students typically
acquire skills both in ear-training and an understanding of the acoustic,
physiological, and perceptual characteristics of speech sounds. But there
is usually less opportunity to test this knowledge on sizeable quantities
of speech data partly because putting together any database that is
sufficient in extent to be able to address non-trivial questions in
phonetics is very time-consuming. In the last ten years, this issue has
been offset somewhat by the rapid growth of national and international
speech corpora which has been driven principally by the needs of speech
technology. But there is still usually a big gap between the knowledge
acquired in phonetics from classes on the one hand and applying this
knowledge to available speech corpora with the aim of solving different
kinds of theoretical problems on the other. The difficulty stems not just
from getting the right data out of the corpus but also in deciding what
kinds of graphical and quantitative techniques are available and
appropriate for the problem that is to be solved. So one of the main
reasons for writing this book is a pedagogical one: it is to bridge this
gap between recently acquired knowledge of experimental phonetics on the
one hand and practice with quantitative data analysis on the other. The
need to bridge this gap is sometimes most acutely felt when embarking for
the first time on a larger-scale project, honours or masters thesis in
which students collect