Using Conditional Random Fields to Predict Pitch Accents in
Conversational Speech
Michelle L. Gregory
Linguistics Department
University at Buffalo
Buffalo, NY 14260
mgregory@buffalo.edu
Yasemin Altun
Department of Computer Science
Brown University
Providence, RI 02912
altun@cs.brown.edu
Abstract
The detection of prosodic characteristics is an im-
portant aspect of both speech synthesis and speech
recognition. Correct placement of pitch accents aids
in more natural sounding speech, while automatic
detection of accents can contribute to better word-
level recognition and better textual understanding.
In this paper we investigate probabilistic, contex-
tual, and phonological factors that influence pitch
accent placement in natural, conversational speech
in a sequence labeling setting. We introduce Con-
ditional Random Fields (CRFs) to pitch accent pre-
diction task in order to incorporate these factors ef-
ficiently in a sequence model. We demonstrate the
usefulness and the incremental effect of these fac-
tors in a sequence model by performing experiments
on hand labeled data from the Switchboard Corpus.
Our model outperforms the baseline and previous
models of pitch accent prediction on the Switch-
board Corpus.
1 Introduction
The suprasegmental features of speech relay critical
information in conversation. Yet, one of the ma-
jor roadblocks to natural sounding speech synthe-
sis has been the identification and implementation
of prosodic characteristics. The difficulty with this
task lies in the fact that prosodic cues are never ab-
solute; they are relative to individual speakers, gen-
der, dialect, discourse context, local context, phono-
logical environment, and many other factors. This is
especially true of pitch accent, the acoustic cues that
make one word more prominent than others in an
utterance. For example, a word with a fundamen-
tal frequency (f0) of 120 Hz would likely be quite
prominent in a male speaker, but not for a typical fe-
male speaker. Likewise, the accent on the utterance
”Jon’s leaving.” is critical in determining whether
it is the answer to the question ”Who is leaving?”
(”JON’s leaving.”) or ”What is Jon doing?” (”Jon’s
LEAVING.”). Accurate pitch accent prediction lies
in the successful combination of as many of the con-
textual variables as possible. Syntactic information
such as part of speech has proven to be a success-
ful predictor of accentuation (Hirschberg, 1993; Pan
and Hirschberg, 2001). In general, function words
are not accented, while content words are. Vari-
ous measures of a word’s informativeness, such as
the information content (IC) of a word (Pan and
McKeown, 1999) and its collocational strength in a
given context (Pan and Hirschberg, 2001) have also
proven to be useful models of pitch accent. How-
ever, in open topic conversational speech, accent is
very unpredictable. Part of speech and the infor-
mativeness of a word do not capture all aspects of
accentuation, as we see in this example taken from
Switchboard, where a function word gets accented
(accented words are in uppercase):
I, I have STRONG OBJECTIONS to THAT.
Accent is also influenced by aspects of rhythm
and timing. The length of words, in both number
of phones and normalized duration, affect its likeli-
hood of being accented. Additionally, whether the
immediately surrounding words bear pitch accent
also affect the likelihood of accentuation. In other
words, a word that might typically be accented may
be unaccented because the surrounding words also
bear pitch accent. Phrase boundaries seem to play
a role in accentuation as well. The first word of in-
tonational phrases (IP) is less likely to be accented
while the last word of an IP tends be accented. In
short, accented words within the same IP are not in-
dependent of each other.
Previous work on pitch accent prediction, how-
ever, neglected the dependency between labels. Dif-
ferent machine learning techniques, such as deci-
sion trees (Hirschberg, 1993), rule induction sys-
tems (Pan and McKeown, 1999), bagging (Sun,
2002), boosting (Sun, 2002) have been used in a
scenario where the accent of each word is pre-
dicted independently. One exception to this line
of research is the use of Hidden Markov Models
(HMM) for pitch accent prediction (Pan and McK-
eown, 1999; Conkie et al., 1999). Pan and McKe-
own (1999) demonstrate the effectiveness of a se-
quence model over a rule induction system, RIP-
PER, that treats each label independently by show-
ing that HMMs outperform RIPPER when the same
variables are used.
Until recently, HMMs were the predominant for-
malism to model label sequences. However, they
have two major shortcomings. They are trained
non-discriminatively using maximum likelihood es-
timation to model the joint probability of the ob-
servation and label sequences. Also, they require
questionable independence assumptions to achieve
efficient inference and learning. Therefore, vari-
ables used in Hidden Markov models of pitch ac-
cent prediction have been very limited, e.g. part of
speech and frequency (Pan and McKeown, 1999).
Discriminative learning methods, such as Maximum
Entropy Markov Models (McCallum et al., 2000),
Projection Based Markov Models (Punyakanok and
Roth, 2000), Conditional Random Fields (Lafferty
et al., 2001), Sequence AdaBoost (Altun et al.,
2003a), Sequence Perceptron (Collins, 2002), Hid-
den Markov Support Vector Machines (Altun et
al., 2003b) and Maximum-Margin Markov Net-
works (Taskar et al., 2004), overcome the limita-
tions of HMMs. Among these methods, CRFs is
the most common technique used in NLP and has
been successfully applied to Part-of-Speech Tag-
ging (Lafferty et al., 2001), Named-Entity Recog-
nition (Collins, 2002) and shallow parsing (Sha and
Pereira, 2003; McCallum, 2003).
The goal of this study is to better identify which
words in a string of text will bear pitch accent.
Our contribution is two-fold: employing new pre-
dictors and utilizing a discriminative model. We
combine the advantages of probabilistic, syntactic,
and phonological predictors with the advantages of
modeling pitch accent in a sequence labeling setting
using CRFs (Lafferty et al., 2001).
The rest of the paper is organized as follows: In
Section 2, we introduce CRFs. Then, we describe
our corpus and the variables in Section 3 and Sec-
tion 4. We present the experimental setup and report
results in Section 5. Finally, we discuss our results
(Section 6) and conclude (Section 7).
2 Conditional Random Fields
CRFs can be considered as a generalization of lo-
gistic regression to label sequences. They define
a conditional probability distribution of a label se-
quence y given an observation sequence x. In this
paper, x = (x1;x2;:::;xn) denotes a sentence of
length n and y = (y1;y2;:::;yn) denotes the la-
bel sequence corresponding to x. In pitch accent
prediction, xt is a word and yt is a binary label de-
noting whether xt is accented or not.
CRFs specify a linear discriminative function F
parameterized by  over a feature representation of
the observation and label sequence  (x;y). The
model is assumed to be stationary, thus the feature
representation can be partitioned with respect to po-
sitions tin the sequence and linearly combined with
respect to the importance of each feature  k, de-
noted by  k. Then the discriminative function can
be stated as in Equation 1:
F(x;y; ) = X
t
h ; t(x;y)i (1)
Then, the conditional probability is given by
p(yjx; ) = 1Z(x; )F(x;y; ) (2)
where Z(x; ) = P yF(x; y; ) is a normaliza-
tion constant which is computed by summing over
all possible label sequences  y of the observation se-
quence x.
We extract two types of features from a sequence
pair:
1. Current label and information about the obser-
vation sequence, such as part-of-speech tag of
a word that is within a window centered at the
word currently labeled, e.g. Is the current word
pitch accented and the part-of-speech tag of
the previous word=Noun?
2. Current label and the neighbors of that label,
i.e. features that capture the inter-label depen-
dencies, e.g. Is the current word pitch accented
and the previous word not accented?
Since CRFs condition on the observation se-
quence, they can efficiently employ feature repre-
sentations that incorporate overlapping features, i.e.
multiple interacting features or long-range depen-
dencies of the observations, as opposed to HMMs
which generate observation sequences.
In this paper, we limit ourselves to 1-order
Markov model features to encode inter-label de-
pendencies. The information used to encode the
observation-label dependencies is explained in de-
tail in Section 4.
In CRFs, the objective function is the log-loss of
the model with  parameters with respect to a train-
ing set D. This function is defined as the negative
sum of the conditional probabilities of each training
label sequence yi, given the observation sequence
xi, where D  f(xi;yi) : i = 1;:::;mg. CRFs are
known to overfit, especially with noisy data if not
regularized. To overcome this problem, we penalize
the objective function by adding a Gaussian prior
(a term proportional to the squared norm jj jj2) as
suggested in (Johnson et al., 1999). Then the loss
function is given as:
L( ;D) =  
mX
i
logp(yijxi; ) + 12cjj jj2
=  
mX
i
F(xi;yi; ) + logZ(xi; )
+ 12cjj jj2 (3)
where c is a constant.
Lafferty et al. (2001), proposed a modification
of improved iterative scaling for parameter estima-
tion in CRFs. However, gradient-based methods
have often found to be more efficient for minimizing
Equation 3 (Minka, 2001; Sha and Pereira, 2003).
In this paper, we use the conjugate gradient descent
method to optimize the above objective function.
The gradients are computed as in Equation 4:
r L =
mX
i
X
t
Ep[ t(xi;y)]   t(xi;yi)
+ c (4)
where the expectation is with respect to all possi-
ble label sequences of the observation sequence xi
and can be computed using the forward backward
algorithm.
Given an observation sequence x, the best label
sequence is given by:
^y = arg maxy F(x;y; ^ ) (5)
where ^ is the parameter vector that minimizes
L( ;D). The best label sequence can be identified
by performing the Viterbi algorithm.
3 Corpus
The data for this study were taken from the Switch-
board Corpus (Godfrey et al., 1992), which con-
sists of 2430 telephone conversations between adult
speakers (approximately 2.4 million words). Partic-
ipants were both male and female and represented
all major dialects of American English. We used a
portion of this corpus that was phonetically hand-
transcribed (Greenberg et al., 1996) and segmented
into speech boundaries at turn boundaries or pauses
of more than 500 ms on both sides. Fragments con-
tained seven words on average. Additionally, each
word was coded for probabilistic and contextual
information, such as word frequency, conditional
probabilities, the rate of speech, and the canonical
pronunciation (Fosler-Lussier and Morgan, 1999).
The dataset used in all analysis in this study con-
sists of only the first hour of the database, comprised
of 1,824 utterances with 13,190 words. These utter-
ances were hand coded for pitch accent and intona-
tional phrase brakes.
3.1 Pitch Accent Coding
The utterances were hand labeled for accents and
boundaries according to the Tilt Intonational Model
(Taylor, 2000). This model is characterized by a
series of intonational events: accents and bound-
aries. Labelers were instructed to use duration, am-
plitude, pausing information, and changes in f0 to
identify events. In general, labelers followed the ba-
sic conventions of EToBI for coding (Taylor, 2000).
However, the Tilt coding scheme was simplified.
Accents were coded as either major or minor (and
some rare level accents) and breaks were either ris-
ing or falling. Agreement for the Tilt coding was
reported at 86%. The CU coding also used a simpli-
fied EToBI coding scheme, with accent types con-
flated and only major breaks coded. Accent and
break coding pair-wise agreement was between 85-
95% between coders, with a kappa  of 71%-74%
where  is the difference between expected agree-
ment and actual agreement.
4 Variables
The label we were predicting was a binary distinc-
tion of accented or not. The variables we used for
prediction fall into three main categories: syntac-
tic, probabilistic variables, which include word fre-
quency and collocation measures, and phonological
variables, which capture aspects of rhythm and tim-
ing that affect accentuation.
4.1 Syntactic variables
The only syntactic category we used was a four-
way classification for hand-generated part of speech
(POS): Function, Noun, Verb, Other, where Other
includes all adjectives and adverbs1. Table 1 gives
the percentage of accented and unaccented items by
POS.
1We also tested a categorization of 14 distinct part of speech
classes, but the results did not improve, so we only report on the
four-way classification.
Accented Unaccented
Function 21% 79%
Verb 59% 41%
Noun 30% 70%
Other 49% 51%
Table 1: Percentage of accented and unaccented
items by POS.
Variable Definition Example
Unigram logp(wi) and, I
Bigram logp(wijwi 1) roughing it
Rev Bigram logp(wijwi+1) rid of
Joint logp(wi 1;wi) and I
Rev Joint logp(wi;wi+1) and I
Table 2: Definition of probabilistic variables.
4.2 Probabilistic variables
Following a line of research that incorporates the
information content of a word as well as collo-
cation measures (Pan and McKeown, 1999; Pan
and Hirschberg, 2001) we have included a number
of probabilistic variables. The probabilistic vari-
ables we used were the unigram frequency, the pre-
dictability of a word given the preceding word (bi-
gram), the predictability of a word given the follow-
ing word (reverse bigram), the joint probability of a
word with the preceding (joint), and the joint prob-
ability of a word with the following word (reverse
joint). Table 2 provides the definition for these,
as well as high probability examples from the cor-
pus (the emphasized word being the current target).
Note all probabilistic variables were in log scale.
The values for these probabilities were obtained
using the entire 2.4 million words of SWBD2. Table
3 presents the Spearman’s rank correlation coeffi-
cient between the probabilistic measures and accent
(Conover, 1980). These values indicate the strong
correlation of accents to the probabilistic variables.
As the probability increases, the chance of an accent
decreases. Note that all values are significant at the
p<:001 level.
We also created a combined part of speech and
unigram frequency variable in order to have a vari-
able that corresponds to the variable used in (Pan
2Our current implementation of CRF only takes categorical
variables, thus for the experiments, all probabilistic variables
were binned into 5 equal categories. We also tried more bins
and produced similar results, so we only report on the 5-binned
categories. We computed correlations between pitch accent and
the original 5 variables as well as the binned variables and they
are very similar.
Variables Spearman’s  
Unigram -.451
Bigram -.309
Reverse Bigram -.383
Joint -.207
Reverse joint -.265
Table 3: Spearman’s correlation values for the prob-
abilistic measures.
and McKeown, 1999).
4.3 Phonological variables
The last category of predictors, phonological vari-
ables, concern aspects of rhythm and timing of an
utterance. We have two main sources for these vari-
ables: those that can be computed solely from a
string of text (textual), and those that require some
sort of acoustic information (acoustic). Sun (2002)
demonstrated that the number of phones in a syl-
lable, the number of syllables in a word, and the
position of a word in a sentence are useful predic-
tors of which syllables get accented. While Sun was
concerned with predicting accented syllables, some
of the same variables apply to word level targets as
well. For our textual phonological features, we in-
cluded the number of syllables in a word and the
number of phones (both in citation form as well as
transcribed form). Instead of position in a sentence,
we used the position of the word in an utterance
since the fragments do not necessarily correspond
to sentences in the database we used. We also made
use of the utterance length. Below is the list of our
textual features:
 Number of canonical syllables
 Number of canonical phones
 Number of transcribed phones
 The length of the utterance in number of words
 The position of the word in the utterance
The main purpose of this study is to better pre-
dict which words in a string of text receive accent.
So far, all of our predictors are ones easily com-
puted from a string of text. However, we have in-
cluded a few variables that affect the likelihood of
a word being accented that require some acoustic
data. To the best of our knowledge, these features
have not been used in acoustic models of pitch ac-
cent prediction. These features include the duration
of the word, speech rate, and following intonational
phrase boundaries. Given the nature of the SWBD
corpus, there are many disfluencies. Thus, we also
Feature  2 Sig
canonical syllables 1636 p<:001
canonical phones 2430 p<:001
transcribed phones 2741 p<:001
utt length 80 p<:005
utt position 295 p<:001
duration 3073 p<:001
speech rate 101 p<:001
following pause 27 p<:001
foll filled pause 328 p<:001
foll IP boundary 1047 p<:001
Table 4: Significance of phonological features on
pitch accent prediction.
included following pauses and filled pauses as pre-
dictors. Below is the list of our acoustic features:
 Log of duration in milliseconds normalized
by number of canonical phones binned into 5
equal categories.
 Log Speech Rate; calculated on strings of
speech bounded on either side by pauses of
300 ms or greater and binned into 5 equal cat-
egories.
 Following pause; a binary distinction of
whether a word is followed by a period of si-
lence or not.
 Following filled pause; a binary distinction of
whether a word was followed by a filled pause
(uh, um) or not.
 Following IP boundary
Table 4 indicates that each of these features sig-
nificantly affect the presence of pitch accent. While
certainly all of these variables are not independent
of on another, using CRFs, one can incorporate all
of these variables into the pitch accent prediction
model with the advantage of making use of the de-
pendencies among the labels.
4.4 Surrounding Information
Sun (2002) has shown that the values immediately
preceding and following the target are good predic-
tors for the value of the target. We also experi-
mented with the effects of the surrounding values
by varying the window size of the observation-label
feature extraction described in Section 2. When the
window size is 1, only values of the word that is la-
belled are incorporated in the model. When the win-
dow size is 3, the values of the previous and the fol-
lowing words as well as the current word are incor-
porated in the model. Window size 5 captures the
values of the current word, the two previous words
and the two following words.
5 Experiments and Results
All experiments were run using 10 fold cross-
validation. We used Viterbi decoding to find the
most likely sequence and report the performance in
terms of label accuracy. We ran all experiments with
varying window sizes (w 2 f1;3;5g). The baseline
which simply assigns the most common label, un-
accented, achieves 60:53  1:50%.
Previous research has demonstrated that part of
speech and frequency, or a combination of these
two, are very reliable predictors of pitch accent.
Thus, to test the worthiness of using a CRF model,
the first experiment we ran was a comparison of an
HMM to a CRF using just the combination of part of
speech and unigram. The HMM score (referred as
HMM:POS, Unigram in Table 5) was 68:62  1:78,
while the CRF model (referred as CRF:POS, Uni-
gram in Table 5) performed significantly better at
72:56  1:86. Note that Pan and McKeown (1999)
reported 74% accuracy with their HMM model.
The difference is due to the different corpora used
in each case. While they also used spontaneous
speech, it was a limited domain in the sense that
it was speech from discharge orders from doctors
at one medical facility. The SWDB corpus is open
domain conversational speech.
In order to capture some aspects of the IC and
collocational strength of a word, in the second ex-
periment we ran part of speech plus all of the prob-
abilistic variables (referred as CRF:POS, Prob in
Table 5). The model accuracy was 73.94%, thus
improved over the model using POS and unigram
values by 1.38%.
In the third experiment we wanted to know if TTS
applications that made use of purely textual input
could be aided by the addition of timing and rhythm
variables that can be gleaned from a text string.
Thus, we included the textual features described in
Section 4.3 in addition to the probabilistic and syn-
tactic features (referred as CRF:POS, Prob, Txt in
Table 5). The accuracy was improved by 1.73%.
For the final experiment, we added the acoustic
variable, resulting in the use of all the variables de-
scribed in Section 4 (referred as CRF:All in Table
5). We get about 0.5% increase in accuracy, 76.1%
with a window of size w = 1.
Using larger windows resulted in minor increases
in the performance of the model, as summarized in
Table 5. Our best accuracy was 76.36% using all
features in a w = 5 window size.
Model:Variables w = 1 w = 3 w = 5
Baseline 60.53
HMM: POS,Unigram 68.62
CRF: POS, Unigram 72.56
CRF: POS, Prob 73.94 74.19 74.51
CRF: POS, Prob, Txt 75.67 75.74 75.89
CRF: All 76.1 76.23 76.36
Table 5: Test accuracy of pitch accent prediction on
SWDB using various variables and window sizes.
6 Discussion
Pitch accent prediction is a difficult task, in that, the
number of different speakers, topics, utterance frag-
ments and disfluent production of the SWBD corpus
only increase this difficulty. The fact that 21% of
the function words are accented indicates that mod-
els of pitch accent that mostly rely on part of speech
and unigram frequency would not fair well with this
corpus. We have presented a model of pitch accent
that captures some of the other factors that influence
accentuation. In addition to adding more probabilis-
tic variables and phonological factors, we have used
a sequence model that captures the interdependence
of accents within a phrase.
Given the distinct natures of corpora used, it is
difficult to compare these results with earlier mod-
els. However, in experiment 1 (HMM: POS, Uni-
gram vs CRF: POS, Unigram) we have shown that
a CRF model achieves a better performance than an
HMM model using the same features. However,
the real strength of CRFs comes from their ability
to incorporate different sources of information effi-
ciently, as is demonstrated in our experiments.
We did not test directly the probabilistic measures
(or collocation measures) that have been used before
for this task, namely information content (IC) (Pan
and McKeown, 1999) and mutual information (Pan
and Hirschberg, 2001). However, the measures we
have used encompass similar information. For ex-
ample, IC is only the additive inverse of our unigram
measure:
IC(w) =  logp(w) (6)
Rather than using mutual information as a measure
of collocational strength, we used unigram, bigram
and joint probabilities. A model that includes both
joint probability and the unigram probabilities of wi
and wi 1 is comparable to one that includes mutual
information.
Just as the likelihood of a word being accented
is influenced by a following silence or IP bound-
ary, the collocational strength of the target word
with the following word (captured by reverse bi-
gram and reverse joint) is also a factor. With the
use of POS, unigram, and all bigram and joint prob-
abilities, we have shown that (a) CRFs outperform
HMMs, and (b) our probabilistic variables increase
accuracy from a model that include POS + unigram
(73.94% compared to 72.56%).
For tasks in which pitch accent is predicted solely
based on a string of text, without the addition of
acoustic data, we have shown that adding aspects
of rhythm and timing aids in the identification of
accent targets. We used the number of words in
an utterance, where in the utterance a word falls,
how long in both number of syllables and number
of phones all affect accentuation. The addition of
these variables improved the model by nearly 2%.
These results suggest that Accent prediction models
that only make use of textual information could be
improved with the addition of these variables.
While not trying to provide a complete model
of accentuation from acoustic information, in this
study we tested a few acoustic variables that have
not yet been tested. The nature of the SWBD cor-
pus allowed us to investigate the role of disfluencies
and widely variable durations and speech rate on ac-
centuation. Especially speech rate, duration and sur-
rounding silence are good predictors of pitch accent.
The addition of these predictors only slightly im-
proved the model (about .5%). Acoustic features are
very sensitive to individual speakers. In the corpus,
there are many different speakers of varying ages
and dialects. These variables might become more
useful if one controls for individual speaker differ-
ences. To really test the usefulness of these vari-
ables, one would have to combine them with acous-
tic features that have been demonstrated to be good
predictors of pitch accent (Sun, 2002; Conkie et al.,
1999; Wightman et al., 2000).
7 Conclusion
We used CRFs with new measures of collocational
strength and new phonological factors that capture
aspects of rhythm and timing to model pitch accent
prediction. CRFs have the theoretical advantage of
incorporating all these factors in a principled and ef-
ficient way. We demonstrated that CRFs outperform
HMMs also experimentally. We also demonstrated
the usefulness of some new probabilistic variables
and phonological variables. Our results mainly have
implications for the textual prediction of accents in
TTS applications, but might also be useful in au-
tomatic speech recognition tasks such as automatic
transcription of multi-speaker meetings. In the near
future we would like to incorporate reliable acoustic
information, controlling for individual speaker dif-
ference and also apply different discriminative se-
quence labeling techniques to pitch accent predic-
tion task.
8 Acknowledgements
This work was partially funded by CAREER award
#IIS 9733067 IGERT. We would also like to thank
Mark Johnson for the idea of this project, Dan Ju-
rafsky, Alan Bell, Cynthia Girand, and Jason Bre-
nier for their helpful comments and help with the
database.
References
Y. Altun, T. Hofmann, and M. Johnson. 2003a.
Discriminative learning for label sequences via
boosting. In Proc. of Advances in Neural Infor-
mation Processing Systems.
Y. Altun, I. Tsochantaridis, and T. Hofmann. 2003b.
Hidden markov support vector machines. In
Proc. of 20th International Conference on Ma-
chine Learning.
M. Collins. 2002. Discriminative training meth-
ods for Hidden Markov Models: Theory and ex-
periments with perceptron algorithms. In Proc.
of Empirical Methods of Natural Language Pro-
cessing.
A. Conkie, G. Riccardi, and R. Rose. 1999.
Prosody recognition from speech utterances us-
ing acoustic and linguistic based models of
prosodic events. In Proc. of EUROSPEECH’99.
W. J. Conover. 1980. Practical Nonparametric
Statistics. Wiley, New York, 2nd edition.
E. Fosler-Lussier and N. Morgan. 1999. Effects of
speaking rate and word frequency on conversa-
tional pronunci ations. In Speech Communica-
tion.
J. Godfrey, E. Holliman, and J. McDaniel. 1992.
SWITCHBOARD: Telephone speech corpus for
research and develo pment. In Proc. of the Inter-
national Conference on Acoustics, Speech, and
Signal Processing.
S. Greenberg, D. Ellis, and J. Hollenback. 1996. In-
sights into spoken language gleaned from pho-
netic transcripti on of the Switchboard corpus.
In Proc. of International Conference on Spoken
Language Processsing.
J. Hirschberg. 1993. Pitch accent in context: Pre-
dicting intonational prominence from text. Artifi-
cial Intelligence, 63(1-2):305–340.
M. Johnson, S. Geman, S. Canon, Z. Chi, and
S. Riezler. 1999. Estimators for stochastic
unification-based grammars. In Proc. of ACL’99
Association for Computational Linguistics.
J. Lafferty, A. McCallum, and F. Pereira. 2001.
Conditional random fields: Probabilistic models
for segmenting and labeling sequence data. In
Proc. of 18th International Conference on Ma-
chine Learning.
A. McCallum, D. Freitag, and F. Pereira. 2000.
Maximum Entropy Markov Models for Infor-
mation Extraction and Segmentation. In Proc.
of 17th International Conference on Machine
Learning.
A. McCallum. 2003. Efficiently inducing features
of Conditional Random Fields. In Proc. of Un-
certainty in Articifical Intelligence.
T. Minka. 2001. Algorithms for maximum-
likelihood logistic regression. Technical report,
CMU, Department of Statistics, TR 758.
S. Pan and J. Hirschberg. 2001. Modeling local
context for pitch accent prediction. In Proc. of
ACL’01, Association for Computational Linguis-
tics.
S. Pan and K. McKeown. 1999. Word informa-
tiveness and automatic pitch accent modeling.
In Proc. of the Joint SIGDAT Conference on
EMNLP and VLC.
V. Punyakanok and D. Roth. 2000. The use of
classifiers in sequential inference. In Proc. of
Advances in Neural Information Processing Sys-
tems.
F. Sha and F. Pereira. 2003. Shallow parsing with
conditional random fields. In Proc. of Human
Language Technology.
Xuejing Sun. 2002. Pitch accent prediction using
ensemble machine learning. In Proc. of the In-
ternational Conference on Spoken Language Pro-
cessing.
B. Taskar, C. Guestrin, and D. Koller. 2004. Max-
margin markov networks. In Proc. of Advances
in Neural Information Processing Systems.
P. Taylor. 2000. Analysis and synthesis of intona-
tion using the Tilt model. Journal of the Acousti-
cal Society of America.
C. W. Wightman, A. K. Syrdal, G. Stemmer,
A. Conkie, and M. Beutnagel. 2000. Percep-
tually Based Automatic Prosody Labeling and
Prosodically Enriched Unit Selection Improve
Concatenative Text-To-Speech Synthesis. vol-
ume 2, pages 71–74.
