Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 1113–1120,
Sydney, July 2006. c©2006 Association for Computational Linguistics
Learning to Say It Well:
Reranking Realizations by Predicted Synthesis Quality
Crystal Nakatsu and Michael White
Department of Linguistics
The Ohio State University
Columbus, OH 43210 USA
a102cnakatsu,mwhitea103@ling.ohio-state.edu
Abstract
This paper presents a method for adapting
a language generator to the strengths and
weaknesses of a synthetic voice, thereby
improving the naturalness of synthetic
speech in a spoken language dialogue sys-
tem. The method trains a discriminative
reranker to select paraphrases that are pre-
dicted to sound natural when synthesized.
The ranker is trained on realizer and syn-
thesizer features in supervised fashion, us-
ing human judgements of synthetic voice
quality on a sample of the paraphrases rep-
resentative of the generator’s capability.
Results from a cross-validation study indi-
cate that discriminative paraphrase rerank-
ing can achieve substantial improvements
in naturalness on average, ameliorating the
problem of highly variable synthesis qual-
ity typically encountered with today’s unit
selection synthesizers.
1 Introduction
Unit selection synthesis1—a technique which con-
catenates segments of natural speech selected from
a database—has been found to be capable of pro-
ducing high quality synthetic speech, especially
for utterances that are similar to the speech in the
database in terms of style, delivery, and coverage
(Black and Lenzo, 2001). In particular, in the lim-
ited domain of a spoken language dialogue sys-
tem, it is possible to achieve highly natural synthe-
sis with a purpose-built voice (Black and Lenzo,
2000). However, it can be difficult to develop
1See e.g. (Hunt and Black, 1996; Black and Taylor, 1997;
Beutnagel et al., 1999).
a synthetic voice for a dialogue system that pro-
duces natural speech completely reliably, and thus
in practice output quality can be quite variable.
Two important factors in this regard are the label-
ing process for the speech database and the direc-
tion of the dialogue system’s further development,
after the voice has been built: when labels are as-
signed fully automatically to the recorded speech,
label boundaries may be inaccurate, leading to un-
natural sounding joins in speech output; and when
further system development leads to the genera-
tion of utterances that are less like those in the
recording script, such utterances must be synthe-
sized using smaller units with more joins between
them, which can lead to a considerable dropoff in
quality.
As suggested by Bulyko and Ostendorf (2002),
one avenue for improving synthesis quality in a di-
alogue system is to have the system choose what
to say in part by taking into account what is likely
to sound natural when synthesized. The idea is
to take advantage of the generator’s periphrastic
ability:2 given a set of generated paraphrases that
suitably express the desired content in the dialogue
context, the system can select the specific para-
phrase to use as its response according to the pre-
dicted quality of the speech synthesized for that
paraphrase. In this way, if there are significant
differences in the predicted synthesis quality for
the various paraphrases—and if these predictions
are generally borne out—then, by selecting para-
phrases with high predicted synthesis quality, the
dialogue system (as a whole) can more reliably
produce natural sounding speech.
In this paper, we present an application of dis-
2See e.g. (Iordanskaja et al., 1991; Langkilde and Knight,
1998; Barzilay and McKeown, 2001; Pang et al., 2003) for
discussion of paraphrase in generation.
1113
criminative reranking to the task of adapting a lan-
guage generator to the strengths and weaknesses
of a particular synthetic voice. Our method in-
volves training a reranker to select paraphrases
that are predicted to sound natural when synthe-
sized, from the N-best realizations produced by
the generator. The ranker is trained in super-
vised fashion, using human judgements of syn-
thetic voice quality on a representative sample of
the paraphrases. In principle, the method can be
employed with any speech synthesizer. Addition-
ally, when features derived from the synthesizer’s
unit selection search can be made available, fur-
ther quality improvements become possible.
The paper is organized as follows. In Section 2,
we review previous work on integrating choice in
language generation and speech synthesis, and on
learning discriminative rerankers for generation.
In Section 3, we present our method. In Section 4,
we describe a cross-validation study whose results
indicate that discriminative paraphrase reranking
can achieve substantial improvements in natural-
ness on average. Finally, in Section 5, we con-
clude with a summary and a discussion of future
work.
2 Previous Work
Most previous work on integrating language gen-
eration and synthesis, e.g. (Davis and Hirschberg,
1988; Prevost and Steedman, 1994; Hitzeman et
al., 1998; Pan et al., 2002), has focused on how
to use the information present in the language
generation component in order to specify contex-
tually appropriate intonation for the speech syn-
thesizer to target. For example, syntactic struc-
ture, information structure and dialogue context
have all been argued to play a role in improving
prosody prediction, compared to unrestricted text-
to-speech synthesis. While this topic remains an
important area of research, our focus is instead
on a different opportunity that arises in a dialogue
system, namely, the possibility of choosing the ex-
act wording and prosody of a response according
to how natural it is likely to sound when synthe-
sized.
To our knowledge, Bulyko and Ostendorf
(2002) were the first to propose allowing the
choice of wording and prosody to be jointly deter-
mined by the language generator and speech syn-
thesizer. In their approach, a template-based gen-
erator passes a prosodically annotated word net-
work to the speech synthesizer, rather than a single
text string (or prosodically annotated text string).
To perform the unit selection search on this ex-
panded input efficiently, they employ weighted
finite-state transducers, where each step of net-
work expansion is then followed by minimiza-
tion. The weights are determined by concatena-
tion (join) costs, relative frequencies (negative log
probabilities) of the word sequences, and prosodic
prediction costs, for cases where the prosody is
not determined by the templates. In a perception
experiment, they demonstrated that by expand-
ing the space of candidate responses, their system
achieved higher quality speech output.
Following (Bulyko and Ostendorf, 2002), Stone
et al. (2004) developed a method for jointly de-
termining wording, speech and gesture. In their
approach, a template-based generator produces
a word lattice with intonational phrase breaks.
A unit selection algorithm then searches for a
low-cost way of realizing a path through this
lattice that combines captured motion samples
with recorded speech samples to create coherent
phrases, blending segments of speech and mo-
tion together phrase-by-phrase into extended ut-
terances. Video demonstrations indicate that natu-
ral and highly expressive results can be achieved,
though no human evaluations are reported.
In an alternative approach, Pan and Weng
(2002) proposed integrating instance-based real-
ization and synthesis. In their framework, sen-
tence structure, wording, prosody and speech
waveforms from a domain-specific corpus are si-
multaneously reused. To do so, they add prosodic
and acoustic costs to the insertion, deletion and
replacement costs used for instance-based surface
realization. Their contribution focuses on how to
design an appropriate speech corpus to facilitate
an integrated approach to instance-based realiza-
tion and synthesis, and does not report evaluation
results.
A drawback of these approaches to integrating
choice in language generation and synthesis is that
they cannot be used with most existing speech syn-
thesizers, which do not accept (annotated) word
lattices as input. In contrast, the approach we in-
troduce here can be employed with any speech
synthesizer in principle. All that is required is
that the language generator be capable of produc-
ing N-best outputs; that is, the generator must be
able to construct a set of suitable paraphrases ex-
1114
pressing the desired content, from which the top
N realizations can be selected for reranking ac-
cording to their predicted synthesis quality. Once
the realizations have been reranked, the top scor-
ing realization can be sent to the synthesizer as
usual. Alternatively, when features derived from
the synthesizer’s unit selection search can be made
available—and if the time demands of the dia-
logue system permit—several of the top scoring
reranked realizations can be sent to the synthe-
sizer, and the resulting utterances can be rescored
with the extended feature set.
Our reranking approach has been inspired by
previous work on reranking in parsing and gen-
eration, especially (Collins, 2000) and (Walker et
al., 2002). As in Walker et al.’s (2002) method for
training a sentence plan ranker, we use our gen-
erator to produce a representative sample of para-
phrases and then solicit human judgements of their
naturalness to use as data for training the ranker.
This method is attractive when there is no suit-
able corpus of naturally occurring dialogues avail-
able for training purposes, as is often the case for
systems that engage in human-computer dialogues
that differ substantially from human-human ones.
The primary difference between Walker et al.’s
work and ours is that theirs examines the impact
on text quality of sentence planning decisions such
as aggregation, whereas ours focuses on the im-
pact of the lexical and syntactic choice at the sur-
face realization level on speech synthesis quality,
according to the strengths and weaknesses of a
particular synthetic voice.
3 Reranking Realizations by Predicted
Synthesis Quality
3.1 Generating Alternatives
Our experiments with integrating language gener-
ation and synthesis have been carried out in the
context of the COMIC3 multimodal dialogue sys-
tem (den Os and Boves, 2003). The COMIC sys-
tem adds a dialogue interface to a CAD-like ap-
plication used in sales situations to help clients re-
design their bathrooms. The input to the system
includes speech, handwriting, and pen gestures;
the output combines synthesized speech, an ani-
mated talking head, deictic gestures at on-screen
objects, and direct control of the underlying appli-
cation.
3COnversational Multimodal Interaction with Computers,
http://www.hcrc.ed.ac.uk/comic/.
Drawing on the materials used in (Foster and
White, 2005) to evaluate adaptive generation in
COMIC, we selected a sample of 104 sentences
from 38 different output turns across three dia-
logues. For each sentence in the set, a variant was
included that expressed the same content adapted
to a different user model or adapted to a differ-
ent dialogue history. For example, a description
of a certain design’s colour scheme for one user
might be phrased as As you can see, the tiles have
a blue and green colour scheme, whereas a vari-
ant expression of the same content for a different
user could be Although the tiles have a blue colour
scheme, the design does also feature green, if the
user disprefers blue.
In COMIC, the sentence planner uses XSLT to
generate disjunctive logical forms (LFs), which
specify a range of possible paraphrases in a nested
free-choice form (Foster and White, 2004). Such
disjunctive LFs can be efficiently realized us-
ing the OpenCCG realizer (White, 2004; White,
2006b; White, 2006a). Note that for the experi-
ments reported here, we manually augmented the
disjunctive LFs for the 104 sentences in our sam-
ple to make greater use of the periphrastic capa-
bilities of the COMIC grammar; it remains for fu-
ture work to augment the COMIC sentence plan-
ner produce these more richly disjunctive LFs au-
tomatically.
OpenCCG includes an extensible API for inte-
grating language modeling and realization. To se-
lect preferred word orders, from among all those
allowed by the grammar for the input LF, we used
a backoff trigram model trained on approximately
750 example target sentences, where certain words
were replaced with their semantic classes (e.g.
MANUFACTURER, COLOUR) for better general-
ization. For each of the 104 sentences in our sam-
ple, we performed 25-best realization from the dis-
junctive LF, and then randomly selected up to 12
different realizations to include in our experiments
based on a simulated coin flip for each realization,
starting with the top-scoring one. We used this
procedure to sample from a larger portion of the
N-best realizations, while keeping the sample size
manageable.
Figure 1 shows an example of 12 paraphrases
for a sentence chosen for inclusion in our sample.
Note that the realizations include words with pitch
accent annotations as well as boundary tones as
separate, punctuation-like words. Generally the
1115
a15 thisa72a3 designa72a3 uses tiles from Villeroy and Bocha72a3
’s Funny Daya72a3 collection LL% .
a15 thisa72a3 designa72a3 is based on the Funny Daya72a3 collec-
tion by Villeroy and Bocha72a3 LL% .
a15 thisa72a3 designa72a3 is based on Funny Daya72a3 LL% , by
Villeroy and Bocha72a3 LL% .
a15 thisa72a3 designa72a3 draws from the Funny Daya72a3 collec-
tion by Villeroy and Bocha72a3 LL% .
a15 thisa72a3 one draws from Funny Daya72a3 LL% , by
Villeroy and Bocha72a3 LL% .
a15 herea76a43a72a3 LH% we have a design that is based on
the Funny Daya72a3 collection by Villeroy and Bocha72a3
LL% .
a15 thisa72a3 designa72a3 draws from Villeroy and Bocha72a3 ’s
Funny Daya72a3 series LL% .
a15 here is a design that draws from Funny Daya72a3 LL% ,
by Villeroy and Bocha72a3 LL% .
a15 thisa72a3 one draws from Villeroy and Bocha72a3 ’s
Funny Daya72a3 collection LL% .
a15 thisa72a3 draws from the Funny Daya72a3 collection by
Villeroy and Bocha72a3 LL% .
a15 thisa72a3 one draws from the Funny Daya72a3 collection by
Villeroy and Bocha72a3 LL% .
a15 here is a design that draws from Villeroy and Bocha72a3
’s Funny Daya72a3 collection LL% .
Figure 1: Example of sampled periphrastic alter-
natives for a sentence.
quality of the sampled paraphrases is very high,
only occasionally including dispreferred word or-
ders such as We here have a design in the family
style, where here is in medial position rather than
fronted.4
3.2 Synthesizing Utterances
For synthesis, OpenCCG’s output realizations are
converted to APML,5 a markup language which
allows pitch accents and boundary tones to be
specified, and then passed to the Festival speech
synthesis system (Taylor et al., 1998; Clark et al.,
2004). Festival uses the prosodic markup in the
text analysis phase of synthesis in place of the
structures that it would otherwise have to predict
from the text. The synthesiser then uses the con-
text provided by the markup to enforce the selec-
4In other examples medial position is preferred, e.g. This
design here is in the family style.
5Affective Presentation Markup Language; see
http://www.cstr.ed.ac.uk/projects/
festival/apml.html.
tion of suitable units from the database.
A custom synthetic voice for the COMIC sys-
tem was developed, as follows. First, a domain-
specific recording script was prepared by select-
ing about 150 sentences from the larger set of tar-
get sentences used to train the system’s n-gram
model. The sentences were greedily selected with
the goals of ensuring that (i) all words (including
proper names) in the target sentences appeared at
least once in the record script, and (ii) all bigrams
at the level of semantic classes (e.g. MANUFAC-
TURER, COLOUR) were covered as well. For the
cross-validation study reported in the next section,
we also built a trigram model on the words in the
domain-specific recording script, without replac-
ing any words with semantic classes, so that we
could examine whether the more frequent occur-
rence of the specific words and phrases in this part
of the script is predictive of synthesis quality.
The domain-specific script was augmented with
a set of 600 newspaper sentences selected for di-
phone coverage. The newspaper sentences make
it possible for the voice to synthesize words out-
side of the domain-specific script, though not
necessarily with the same quality. Once these
scripts were in place, an amateur voice talent was
recorded reading the sentences in the scripts dur-
ing two recording sessions. Finally, after the
speech files were semi-automatically segmented
into individual sentences, the speech database was
constructed, using fully automatic labeling.
We have found that the utterances synthesized
with the COMIC voice vary considerably in their
naturalness, due to two main factors. First, the
system underwent further development after the
voice was built, leading to the addition of a va-
riety of new phrases to the system’s repertoire, as
well as many extra proper names (and their pro-
nunciations); since these names and phrases usu-
ally require going outside of the domain-specific
part of the speech database, they often (though not
always) exhibit a considerable dropoff in synthe-
sis quality.6 And second, the boundaries of the au-
tomatically assigned unit labels were not always
accurate, leading to problems with unnatural joins
and reduced intelligibility. To improve the reliabil-
ity of the COMIC voice, we could have recorded
more speech, or manually corrected label bound-
6Note that in the current version of the system, proper
names are always required parts of the output, and thus the
discriminative reranker cannot learn to simply choose para-
phrases that leave out problematic names.
1116
aries; the goal of this paper is to examine whether
the naturalness of a dialogue system’s output can
be improved in a less labor-intensive way.
3.3 Rating Synthesis Quality
To obtain data for training our realization reranker,
we solicited judgements of the naturalness of the
synthesized speech produced by Festival for the
utterances in our sample COMIC corpus. Two
judges (the first two authors) provided judgements
on a 1–7 point scale, with higher scores represent-
ing more natural synthesis. Ratings were gathered
using WebExp2,7 with the periphrastic alternatives
for each sentence presented as a group in a ran-
domized order. Note that for practical reasons,
the utterances were presented out of the dialogue
context, though both judges were familiar with the
kinds of dialogues that the COMIC system is ca-
pable of.
Though the numbers on the seven point scale
were not assigned labels, they were roughly taken
to be “horrible,” “poor,” “fair,” “ok,” “good,” “very
good” and “perfect.” The average assigned rating
across all utterances was 4.05 (“ok”), with a stan-
dard deviation of 1.56. The correlation between
the two judges’ ratings was 0.45, with one judge’s
ratings consistently higher than the other’s.
Some common problems noted by the judges
included slurred words, especially the sometimes
sounding like ther or even their; clipped words,
such as has shortened at times to the point of
sounding like is, or though clipped to unintelligi-
bility; unnatural phrasing or emphasis, e.g. occa-
sional pauses before a possessive ’s, or words such
as style sounding emphasized when they should
be deaccented; unnatural rate changes; “choppy”
speech from poor joins; and some unintelligible
proper names.
3.4 Ranking
While Collins (2000) and Walker et al. (2002)
develop their rankers using the RankBoost algo-
rithm (Freund et al., 1998), we have instead cho-
sen to use Joachims’ (2002) method of formu-
lating ranking tasks as Support Vector Machine
(SVM) constraint optimization problems.8 This
choice has been motivated primarily by conve-
nience, as Joachims’ SVMa108a105a103a104a116 package is easy to
7http://www.hcrc.ed.ac.uk/web exp/
8See (Barzilay and Lapata, 2005) for another application
of SVM ranking in generation, namely to the task of ranking
alternative text orderings for local coherence.
use; we leave it for future work to compare the
performance of RankBoost and SVMa108a105a103a104a116 on our
ranking task.
The ranker takes as input a set of paraphrases
that express the desired content of each sentence,
optionally together with synthesized utterances
for each paraphrase. The output is a ranking of
the paraphrases according to the predicted natu-
ralness of their corresponding synthesized utter-
ances. Ranking is more appropriate than classifi-
cation for our purposes, as naturalnesss is a graded
assessment rather than a categorical one.
To encode the ranking task as an SVM con-
straint optimization problem, each paraphrase a106
of a sentence a105 is represented by a feature vector
a8a40a115a105a106a41 a61 a104a102a49a40a115a105a106a41a59a58a58a58a59a102a109a40a115a105a106a41a105, where a109 is the
number of features. In the training data, the fea-
ture vectors are paired with the average value of
their corresponding human judgements of natural-
ness. From this data, ordered pairs of paraphrases
a40a115a105a106a59a115a105a107a41 are derived, where a115a105a106 has a higher nat-
uralness rating than a115a105a107. The constraint optimiza-
tion problem is then to derive a parameter vector
a126a119 that yields a ranking score function a126a119 a1 a8a40a115a105a106a41
which minimizes the number of pairwise rank-
ing violations. Ideally, for every ordered pair
a40a115a105a106a59a115a105a107a41, we would have a126a119 a1 a8a40a115a105a106a41 a62 a126a119 a1 a8a40a115a105a107a41;
in practice, it is often impossible or intractable to
find such a parameter vector, and thus slack vari-
ables are introduced that allow for training errors.
A parameter to the algorithm controls the trade-off
between ranking margin and training error.
In testing, the ranker’s accuracy can be deter-
mined by comparing the ranking scores for ev-
ery ordered pair a40a115a105a106a59a115a105a107a41 in the test data, and
determining whether the actual preferences are
borne out by the predicted preference, i.e. whether
a126a119 a1 a8a40a115a105a106a41 a62 a126a119 a1 a8a40a115a105a107a41 as desired. Note that
the ranking scores, unlike the original ratings, do
not have any meaning in the absolute sense; their
import is only to order alternative paraphrases by
their predicted naturalness.
In our ranking experiments, we have used
SVMa108a105a103a104a116 with all parameters set to their default
values.
3.5 Features
Table 1 shows the feature sets we have investigated
for reranking, distinguished by the availability of
the features and the need for discriminative train-
ing. The first row shows the feature sets that are
1117
Table 1: Feature sets for reranking.
Discriminative
Availability no yes
Realizer NGRAMS WORDS
Synthesizer COSTS ALL
available to the realizer. There are two n-gram
models that can be used to directly rank alterna-
tive realizations: NGRAM-1, the language model
used in COMIC, and NGRAM-2, the language
model derived from the domain-specific recording
script; for feature values, the negative logarithms
are used. There are also two WORDS feature
sets (shown in the second column): WORDS-BI,
which includes NGRAMS plus a feature for every
possible unigram and bigram, where the value of
the feature is the count of the unigram or bigram
in a given realization; and WORDS-TRI, which
includes all the features in WORDS-BI, plus a
feature for every possible trigram. The second
row shows the feature sets that require informa-
tion from the synthesizer. The COSTS feature set
includes NGRAMS plus the total join and target
costs from the unit selection search. Note that a
weighted sum of these costs could be used to di-
rectly rerank realizations, in much the same way
as relative frequencies and concatenation costs are
used in (Bulyko and Ostendorf, 2002); in our
experiments, we let SVMa108a105a103a104a116 determine how to
weight these costs. Finally, there are two ALL fea-
ture sets: ALL-BI includes NGRAMS, WORDS-
BI and COSTS, plus features for every possi-
ble phone and diphone, and features for every
specific unit in the database; ALL-TRI includes
NGRAMS, WORDS-TRI, COSTS, and a feature
for every phone, diphone and triphone, as well as
specific units in the database. As with WORDS,
the value of a feature is the count of that feature in
a given synthesized utterance.
4 Cross-Validation Study
To train and test our ranker on our feature sets,
we partitioned the corpus into 10 folds and per-
formed 10-fold cross-validation. For each fold,
90% of the examples were used for training the
ranker and the remaining unseen 10% were used
for testing. The folds were created by randomly
choosing from among the sentence groups, result-
ing in all of the paraphrases for a given sentence
occurring in the same fold, and each occurring ex-
Table 2: Comparison of results for differing fea-
ture sets, topline and baseline.
Features Mean Score SD Accuracy (%)
BEST 5.38 1.11 100.0
WORDS-TRI 4.95 1.24 77.3
ALL-BI 4.95 1.24 77.9
ALL-TRI 4.90 1.25 78.0
WORDS-BI 4.86 1.28 76.8
COSTS 4.69 1.27 68.2
NGRAM-2 4.34 1.38 56.2
NGRAM-1 4.30 1.29 53.3
RANDOM 4.11 1.22 50.0
actly once in the testing set as a whole.
We evaluated the performance of our ranker
by determining the average score of the best
ranked paraphrase for each sentence, under each
of the following feature combinations: NGRAM-
1, NGRAM-2, COSTS, WORDS-BI, WORDS-
TRI, ALL-BI, and ALL-TRI. Note that since we
used the human ratings to calculate the score of
the highest ranked utterance, the score of the high-
est ranked utterance cannot be higher than that
of the highest human-rated utterance. Therefore,
we effectively set the human ratings as the topline
(BEST). For the baseline, we randomly chose an
utterance from among the alternatives, and used
its associated score. In 15 tests generating the ran-
dom scores, our average scores ranged from 3.88–
4.18. We report the median score of 4.11 as the
average for the baseline, along with the mean of
the topline and each of the feature subsets, in Ta-
ble 2.
We also report the ordering accuracy of each
feature set used by the ranker in Table 2. As men-
tioned in Section 3.4, the ordering accuracy of the
ranker using a given feature set is determined by
a99a61a78, where a99 is the number of correctly ordered
pairs (of each paraphrase, not just the top ranked
one) produced by the ranker, and a78 is the total
number of human-ranked ordered pairs.
As Table 2 indicates, the mean of BEST is 5.38,
whereas our ranker using WORDS-TRI features
achieves a mean score of 4.95. This is a difference
of 0.42 on a seven point scale, or only a 6% dif-
ference. The ordering accuracy of WORDS-TRI
is 77.3%.
We also measured the improvement of our
ranker with each feature set over the random base-
line as a percentage of the maximum possible
gain (which would be to reproduce the human
topline). The results appear in Figure 2. As the
1118
0
10203040506070
NGRAM-1
NGRAM-2
COSTS
WORDS-BI
ALL-TRI
ALL-BI
WORDS-TRI
Figure 2: Improvement as a percentage of the
maximum possible gain over the random baseline.
figure indicates, the maximum possible gain our
ranker achieves over the baseline is 66% (using the
WORDS-TRI or ALL-BI feature set) . By com-
parison, NGRAM-1 and NGRAM-2 achieve less
than 20% of the possible gain.
To verify our main hypothesis that our ranker
would significantly outperform the baselines,
we computed paired one-tailed a116-tests between
WORDS-TRI and RANDOM (a116 a61 a50a58a52, a112 a60
a56a58a57a120a49a48a0a49a51), and WORDS-TRI and NGRAM-1
(a116 a61 a49a58a52, a112 a60 a52a58a53a120a49a48a0a56). Both differences were
highly significant. We also performed seven post-
hoc comparisons using two-tailed a116-tests, as we
did not have an a priori expectation as to which
feature set would work better. Using the Bonfer-
roni adjustment for multiple comparisons, the a112-
value required to achieve an overall level of signif-
icance of 0.05 is 0.007. In the first post-hoc test,
we found a significant difference between BEST
and WORDS-TRI (a116 a61 a56a58a48,a112 a60 a49a58a56a54a120a49a48a0a49a50),
indicating that there is room for improvement of
our ranker. However, in considering the top scor-
ing feature sets, we did not find a significant dif-
ference between WORDS-TRI and WORDS-BI
(a116 a61 a50a58a51, a112 a60 a48a58a48a50a50), from which we infer that the
difference among all of WORDS-TRI, ALL-BI,
ALL-TRI and WORDS-BI is not significant also.
This suggests that the synthesizer features have
no substantial impact on our ranker, as we would
expect ALL-TRI to be significantly higher than
WORDS-TRI if so. However, since COSTS does
significantly improve upon NGRAM2 (a116 a61 a51a58a53,
a112 a60 a48a58a48a48a49), there is some value to the use of syn-
thesizer features in the absence of WORDS. We
also looked at the comparison for the WORDS
models and COSTS. While WORDS-BI did not
perform significantly better than COSTS ( a116 a61
a50a58a51, a112 a60 a48a58a48a50a53), the added trigrams in WORDS-
TRI did improve ranker performance significantly
over COSTS (a116 a61 a51a58a55, a112 a60 a51a58a50a57a120a49a48a0a52). Since
COSTS ranks realizations in the much the same
way as (Bulyko and Ostendorf, 2002), the fact that
WORDS-TRI outperforms COSTS indicates that
our discriminative reranking method can signifi-
cantly improve upon their non-discriminative ap-
proach.
5 Conclusions
In this paper, we have presented a method for
adapting a language generator to the strengths
and weaknesses of a particular synthetic voice by
training a discriminative reranker to select para-
phrases that are predicted to sound natural when
synthesized. In contrast to previous work on
this topic, our method can be employed with any
speech synthesizer in principle, so long as fea-
tures derived from the synthesizer’s unit selec-
tion search can be made available. In a case
study with the COMIC dialogue system, we have
demonstrated substantial improvements in the nat-
uralness of the resulting synthetic speech, achiev-
ing two-thirds of the maximum possible gain, and
raising the average rating from “ok” to “good.” We
have also shown that in this study, our discrimina-
tive method significantly outperforms an approach
that performs selection based solely on corpus fre-
quencies together with target and join costs.
In future work, we intend to verify the results
of our cross-validation study in a perception ex-
periment with na¨ıve subjects. We also plan to in-
vestigate whether additional features derived from
the synthesizer can better detect unnatural pauses
or changes in speech rate, as well as F0 contours
that fail to exhibit the targeting accenting pattern.
Finally, we plan to examine whether gains in qual-
ity can be achieved with an off-the-shelf, general
purpose voice that are similar to those we have ob-
served using COMIC’s limited domain voice.
Acknowledgements
We thank Mary Ellen Foster, Eric Fosler-Lussier
and the anonymous reviewers for helpful com-
ments and discussion.
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