PACLIC 24 Proceedings

281

Combination of 3 Types of Speech Recognizers
for Anaphora Resolution
Kazutaka Shimada, Noriko Tanamachi, and Tsutomu Endo
Department of Artificial Intelligence, Kyushu Institute of Technology
680-4 Iizuka Fukuoka Japan 820-8502
shimada, n tanamachi, endo@pluto.ai.kyutech.ac.jp

Abstract. In this paper, we propose a method for anaphora resolution in speech understanding for a livelihood support robot. For robust speech recognition, we combine two
types of speech recognizers; a large vocabulary continuous speech recognizer (LVCSR) anddomain-specific speech recognizers (DSSR). One problem in the anaphora resolution is lackof the antecedent in the outputs. To solve the problem, we introduce 2 types of DSSRs; one
medium-scale DSSR and several small DSSRs. In this paper, we describe the basic idea of
our multiple speech recognizer first. The selection process in the recognizer is based on thesimilarity between the LVCSR and each DSSR. Then, by using the outputs from the LVCSR
and the medium-scale DSSR, we resolve anaphoric expressions in the current output from asmall-scale DSSR. The experimental result shows the effectiveness of our method.
Keywords: Anaphora resolution, Multiple speech recognizer, Combination

1 Introduction
Speech understanding and dialogue systems have been developed for practical use recently. Thesesystems often recognize user utterances incorrectly. It is important to deal with speech recognition
errors for speech understanding systems. Extracting keywords and understanding an utterance
using them reduce speech recognition errors (Bouwman et al., 1999; Komatani and Kawahara,
2000). Combining some recognizers is one of the best approaches to improve the accuracy ofspeech understanding systems (Isobe et al., 2007; Utsuro et al., 2004). Utsuro et. al. (2004)
have obtained high accuracy by using some speech recognizers’ outputs. However they dealt withword error reduction only. Although Isobe et. al. (2007) have proposed a multi-domain speech
recognition system based on some domain-specific recognizers, their system cannot treat out-ofdomain utterances such as a chat between users. However chat utterances often include significant
information as the context of the dialogue.
In this paper we propose a simple and effective speech understanding method based on a large
vocabulary continuous speech recognizer (LVCSR) and some domain-specific speech recognizers(DSSR). We call it “One Generalist and Some Specialists (OGSS) model”. Figure 1 (a) shows theoutline of the model. In our system, the LVCSR is the generalist, namely domain-independent,
and the DSSRs are specialists, namely domain-dependent. We focus on the difference between
outputs generated from the generalist and specialists. By using this method, we can recognize domain-dependent speech inputs with high accuracy and also handle context informationin domain-independent speech inputs.
The task of this system is speech understanding for a livelihood support robot. The DSSRs
recognize particular utterances about orders; e.g., order utterances from elders who need care andorder utterances from nurses. We construct the grammar-based DSSRs for order utterances withThis research was supported by NEDO, Intelligent RT Software Project, 2010.
Copyright 2010 by Kazutaka Shimada, Noriko Tanamachi, and Tsutomu Endo

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OGSS model (One Generalist and Some Specialists)
Input

Everyone listensLVCSR

DSSR with small vocabulary
Specialist
Order Specialist

LVCSR
Generalist

DSSR for Order
Non-Order Utterance

I dropped a remote controller under this bed....
*Select

Recognition? (misrecognize)

remote controller bed
Question Specialist

Order Utterance
The most similar : the best output* Generalist* Specialist

There is no limitation for

There are many out-of -domain

recognition, but low accuracy

Please pick it up.

Explanation Specialist* LVCSR often
makes a mistakeutterances, but high accuracy

Integration

Context
information*Select

Recognition

big it up

pick it up
remote“it” = controller(a)Results withhigh accuracy(b)
Figure 1: The OGSS model and the effectivenessa small vocabulary and high accuracy for each order type. We use the LVCSR for recognitionof utterances that the DSSRs can not recognize, such as a chat between users. The information
recognized by the LVCSR is of assistance for context construction of a dialogue. If we handle thesedifferent speech recognizers selectively and integratively, we realize a flexible and robust speech
understanding method. Figure 1 (b) shows the effectiveness of the proposed multiple recognizer.
The DSSR achieves the order recognition with high accuracy and the LVCSR supplies lack of
information in the order utterances.
In general, there are many anaphoric expressions in a dialogue. Anaphora resolution is oneof the most important tasks for understanding the dialogue. In this paper, we also propose ananaphora resolution method in the multiple recognizer. By using previous outputs from the LVCSR
and some DSSRs, we resolve an anaphora in the current output. For example, with respect to theutterance “Please pick it up” in Figure 1 (b), the system identifies that the word “it” in the utterance
is the phrase “remote controller” which was recognized by the LVCSR in the previous utterance.
The antecedent often appears in non-order utterances, that is outside of DSSRs. Therefore thetarget word is usually recognized by a LVCSR. However, the accuracy of the LVCSR is generally
insufficient. The low accuracy of the detection of the antecedent in the speech recognition processleads to the decrease of the accuracy of the anaphora resolution process because the antecedent
does not exist in the output of the speech recognizer. Here we apply a medium-scale DSSR to themultiple recognizer. It contains words of the target situation. In other words, the vocabulary of
the medium-scale DSSR consists of the union of each small-scale DSSR, such as a nurse’s order
DSSR and a patient’s order DSSR. By using the medium-scale DSSR, the accuracy of non-orderutterances often improves. It leads to the improvement of the accuracy of the anaphora resolution
method.
In Section 2, we explain the basic idea of the multiple speech recognizer. In other words, it is
to select an output from each recognizer. In Section 3, we describe an anaphora resolution method
based on the combination of 3 types of speech recognizers. Then, we evaluate the method in termsof the output selection and anaphora resolution in Section 4. Finally we conclude this paper in
Section 5.

2 Combination model
2.1 Basic idea
In this section, we explain the process of output selection in the OGSS model. In this process,we focus on a difference of outputs generated from each recognizer. Even human beings tend
to misunderstand words which consist of similar pronunciations (Komatani et al., 2005). Here

PACLIC 24 Proceedings

283*Utterance-level
Input: saifu wo mottekite(Bring my wallet)
DSSR:

walletbrings a i f u o m o q t e k i t e
Insert, Replace or Delete

LVCSR: s a i g o o m o q t e
finallythink

The Distance is 6.

ED utter = Distance / # of phonemes in two results= 6 / 26 = 0.23

*Word-level
Input: yuka no ue no taoru wo hirotte kureru?
(Could you pick up the towel on the floor?)
DSSR:

floor
on
towel
pick upyuka|noue|no| taoru |o| hiroqte |kureru
2

0

3

0

LVCSR: y u k a | n o | n a k a | w a | r o | o | h i r o q | t e | k u r e r u
floor
in
road
pick upBest Combinations: noue vs. no, no vs. no, taoru vs. wa|ro, hiroqte vs. hiroq|teDistance/Phoneme: 2/6=0.33,
0/4=0,
3/9=0.33,
0/14=0EDword = Sum / # of words in the DSSR result = (0.33+0.33)/7 = 0.09

Figure 2: The edit distance calculation

we focus on the output of the LVCSR. If an input is an order utterance, a DSSR and the LVCSR
generate similar outputs on phoneme-level because the LVCSR is domain independent. On theother hand, if the input is not an order utterance, they often generate different outputs even on thephoneme-level because the DSSR never generates the correct result for non-order utterances.
In this paper, we apply an unsupervised approach to the output selection method. We use theedit distance as the similarity measure. The correspondence such as the edit distance is one of themost effective measures to identify high confidence words in outputs (Utsuro et al., 2004) and to
extract similar word pairs (Komatani et al., 2005). In our method, if an input is an order utterance,
the edit distance between the outputs from a DSSR and the LVCSR becomes small. However if
the input is not an order utterance, that between the outputs from each DSSR and the LVCSR
becomes large. In our method, we compute the edit distance of utterance-level and word-level byusing a DP matching algorithm. In the process, we compute the edit distance between phonemesof words for both levels.
The rules to judge an utterance are applied in the following order:
1. Compute the edit distance of the utterance-level (ED  ) between the LVCSR and each
DSSR. For the outputs of which the edit distance is less than thresh  , we select the outputof the DSSR which contains the minimum ED  as the final output.
2. Compute the edit distance of the word-level (ED ) between the LVCSR and each DSSR.
For the output of which the edit distance is less than thresh , we select the output of the
DSSR which contains the minimum ED as the final output. Otherwise, the LVCSR as
the final output.
The ED  is the edit distance value on the utterance-level. The ED is the average of the edit
distance value computed on word-level. These values are normalized by the number of phonemesin the outputs. The thresh  and thresh are threshold values for the judgment. These values
are decided experimentally.
In the computation of the word-level, we eliminate word pairs that are matched completely
first. Next, we compute all the combinations of the other. Finally, we employ the minimum
combinations as the word-level edit distance. Figure 2 shows an example of the calculation of
the ED  and ED . In the figure, the dotted line denotes completely matched words. The
numerals with arrows denote the original edit distance of the word pair. In the alignment process

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Speech Input

Medium-scale
DSSR

LVCSR

Small-scale
DSSRs

Utterance verification
AnalysisContent word extraction
Dialogue log
DL 1

Dialogue log
DL 2Order utterance

Anaphora resolution
OutputFigure 3: The anaphora resolution with the multiple recognizer.

of word pairs, we select pairs which have the minimum value of the edit distance. In other words,
we admit overlap of word pairs. For example, noue vs. no and no vs. no in Figure 2.

2.2 Recognizers
The OGSS model consists of a LVCSR and some DSSR. The LVCSR is used for utterance verification, namely output selection, and capturing the context information in a dialogue. However,
the accuracy of the LVCSR is generally insufficient. The accuracy is important for an anaphoraresolution process. The low accuracy of the LVCSR leads to the decrease of the accuracy of theanaphora resolution process because the antecedent does not exist in the output of the speech
recognizer.
In this paper, we used 2 types of DSSRs; some small-scale DSSRs and a medium-scale DSSR.
The small-scale DSSRs are used for each particular domain or task; e.g., order utterances fromelders who need care and order utterances from nurses. On the other hand, the medium-scale
DSSR is used for capturing the context in the target situation (a livelihood support robot in thispaper). In other words, it is a integrated DSSR of small-scale DSSRs. The vocabulary of themedium-scale DSSR is the union of each small-scale DSSR.
As a result, the multiple speech recognizer consists of one LVCSR, one medium-scale DSSR
and some small-scale DSSRs. In the utterance verification process, our method compares the
LVCSR with some small-scale DSSRs for the output selection. Also it compares the LVCSR withthe medium-scale DSSR for generating a context word list with high accuracy from the mediumscale DSSR.

3 Understanding and Anaphora Resolution
In this section we explain an anaphora resolution process in the OGSS model. Figure 3 shows theoutline of the anaphora resolution process. In the figure,½ is a content word list detected fromthe output of the medium-scale DSSR. In the utterance verification process, if the output fromthe medium-scale DSSR is similar to that from the LVCSR, content words in the DSSR’s output
are stored. In the same way, if an input is out-of-vocabulary in all DSSRs, that is the large edit
distance between the LVCSR and all the small-scale DSSR, the output of the LVCSR is stored to¾ . Otherwise, the output from a small-scale DSSR is stored to
¾.







PACLIC 24 Proceedings285

For small-scale DSSR outputs
I want to drink the canned drink on the table.
A Grammar in DSSRs
S->Sub Verb to Drink_V Drink_N (in/on/...) Location

Bring (it)*[Bring(Want) [zero pronoun, obj]][Drink(Want),[[`canned drink’ , obj], `on the table’ , loc]]* In Japanese, the word ``it’’ is often omitted.

(a)
For LVCSR and medium-scale DSSR outputs
Mr. Tanaka is in the consulting room.
Dictionary
consulting room -> loc

Rules
Mr. *** -> agt[Tanaka, agt] [consulting room, loc](b)

Figure 4: Examples of the output analysis

3.1 Understanding of Outputs from OGSS model
The output in the previous section, namely the output selection process, is an output of a speech
recognizer. For the anaphora resolution process, we need to analyze the output.
For outputs from small-scale DSSRs, we convert them into a semantic frame. We utilize grammar information of DSSRs for the process. Each DSSR consists of 100-200 words and approximately 100 grammar patterns including approximately 50 categories. Figure 4 (a) shows an example of the grammar pattern and categories. The categories often contain semantic constraints
such as “Drink N” and “Location”. In this process, we also use a dictionary which is described
for required slots of each verb. We detect zero pronouns in utterances from the small-scale DSSRs
by using the dictionary.
For outputs from a LVCSR, we extract keywords by using some rules based on surface expression. For outputs of a medium-scale DSSR, we also extract keywords by using the categories inthe vocabulary. Figure 4 (b) shows examples of the process. In the figure, “obj”, “loc” and “agt”
denote case markers.

3.2 Anaphora Resolution
If an utterance contains an anaphoric expression, our system detects the antecedent from previous
utterances. The anaphora resolution process is based on a scoring method for words in
½ and
.
In
the
scoring
method,
we
also
focus
on
(1)
the
distancefrom
thecurrent
utterance
and(2)¾change of situation.
First, we explain the 1st step of the scoring method; weighting of each word. For a word  in
½ and
¾ , we set the weights in the following manner. The denotes the location of the in
the
½ and
¾.
For the dialogue logs from the medium-scale DSSR ( ½ ),
















 ½ 







½

(1)

where
 denotes the confidence measure computed from the LVCSR or the medium-scale
DSSR for each word and the range is [0, 1].
The¾ contains 3 types of outputs; outputs from the LVCSR, original outputs from thesmall-scale DSSRs and outputs from anaphora resolution. For the LVCSR and original DSSR’s



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outputs, we use the as the weight.

 ¾ 




¾

(2)

If the  is the output of the anaphora resolution process, we set a constant number.

 ¾  (3)

The reason why the value is constant and small as compared with that of original outputs is thatthe accuracy of the anaphora resolution process is not always high, that is insufficient confidence.
Next, we compute a score for each  . Here we apply a decay factor based on the distance andthe situation to the scoring process.



 ¢ 
 ¾





(4)

is the distance between the current utterance that contains the anaphoric expression
where
and the previous utterance that contains the antecedent.
is a parameter for the change of situation. We define “change of a speaker” and “change of the location of a robot” in a dialogue as the“change of situation”. The “change of situation” denotes the change of the topic in conversation.
is the number of changes. If there is no change of situation for a target word  , the is 0. In½ and the
½ by the decay factor . We multiply thethis paper, we set
.











 ½  ½ ¢  ¾  ¾ ¢ 







(5)
(6)

The final score of  is computed as follows:   ¢  ½   ¢  ¾
(7)
where  and  are weight parameters for each  . We compute scores of all candidates  thatappear in previous -utterances, and select the word that contains the maximum score in them.
,  and . Here is the weight for the outputs fromIn this paper, we set
the medium-scale DSSR and is the weight for the outputs from the LVCSR and small DSSRs.
In this scoring, we set a small value to as compared with . The reason is that the outputs fromthe medium-scale DSSR often contain insertion errors because there are many out-of-vocabulary
words in a chat.













4 Experiment and discussion
4.1 Speech recognizer in the experiment
We used Julius as the LVCSR and Julian as the DSSR (Lee et al., 2001). Julius is a famous large
vocabulary continuous speech recognition decoder based on word N-gram and context-dependent
HMM. In this experiment, we used original acoustic and language models. The Julian consists ofa vocabulary and a grammar file. For the grammar file we describe sentence structures in a BNF
style, using word category names as terminal symbols. The vocabulary file defines words with
their pronunciations (i.e., phoneme sequences) for each category. Here we design grammar andvocabulary files of the Julian which accepts only specific utterances from users. In this experiment,
we used 4 small-scale DSSRs that we constructed by hand. The DSSRs are as follows:¯ Order Utterances from patients: e.g., “Please bring the remote controller on the table”¯ Order Utterances from nurses: e.g., “Carry these meals to patient’s rooms”

PACLIC 24 Proceedings

287¯ System Commands: e.g., “Move to the right by 50cm”¯ Question Utterances: e.g., “Where is my cellphone?”
Each DSSR consists of approximately 200 words and 100 grammar patterns.
For the medium-scale DSSR for the anaphora resolution, we also used the Julian. The vocabulary file contained words in all small-scale DSSRs. Since the purpose of the medium-scale
DSSR is to capture words in non-order utterances, the accuracy on sentence-level is not always
important. However, it needs to handle spontaneous speech utterances. Therefore, the grammar
file consisted of the combination of the words in a fixed length; e.g., Noun-PP-Noun-PP-Verb.

4.2 Results
First we evaluated the output selection with a dataset which consists of 20 utterances for each
DSSR and 20 out-of-domain utterances such as greetings. The number of test subjects was 10. In
other words, we evaluated our method with 1000 utterances: 5 categories (4 DSSRs1 and LVCSR)¢ 20 utterances ¢ 10 test subjects. The thresh  and thresh were 0.26 and 0.08 respectively.
These thresholds were determined on a preliminary experiment with another dataset.
The F-value of the output selection was 0.916 on average. In addition, the word recognition
accuracy of each DSSR was 0.940 on average. Besides, we verified that the change of the F-value
was small even if we changed the thresholds within the compass of 0.20-0.26 2 . Therefore, ourmethod, which was based on the edit distance, for the output selection in a multiple recognizer issimple and robust.
Next, we evaluated the anaphora resolution process in our method combining 3 types of speech
recognizers. The dataset of this experiment consisted of 206 utterances that included 53 anaphoric
expressions. Figure 5 shows an example of a dialogue in this experiment. In the figure, “###”denotes “change of a speaker” or “change of the location of a robot”. The number of test subjects
was 2 persons.
Table 1 shows the experimental result. The baseline in the table denotes our method without themedium-scale DSSR. In other words, the method did not handle the dialogue log½ . “Related
work” is a scoring based anaphora resolution method which has been proposed by (Shimada et al.,32009). It accumulated the scores of each candidate in the -previous utterances ( ) .
To compare the related work with our method fairly, we also applied the medium-scale DSSR to it.
The proposed method with the medium-scale DSSR outperformed the baseline, namely a method
without the medium-scale DSSR, and the related work, namely another method with the mediumscale DSSR. By using the medium-scale DSSR, the recognition accuracy of words in non-orderutterances increased. It led to improvement of the accuracy of the anaphora resolution (64.2 versus71.7). This result shows the effectiveness of our method incorporating the medium-scale DSSR
for the anaphora resolution. The related work was based on the summation of the scores of all
candidates in the log. In such method, the existence of noise words, that is insertion errors fromthe speech recognizer, leads to lower accuracy of the anaphora resolution (68.9 versus 71.7).
Although the accuracy of the anaphora resolution increased by using the medium-scale DSSR,
the word accuracy for antecedents was insufficient. Misrecognized words caused the decreaseof the anaphora resolution process, especially deletion errors of the speech recognizer. If theoutputs of speech recognizers and the resolved anaphoric expressions in previous utterances werecompletely correct, that is the oracle data, the accuracy of the anaphora resolution became morethan 95%. This result shows the significance of the accuracy of speech recognizers that captures
the words in non-order utterances. On the other hand, the grammars of our medium-scale DSSR
were not considered carefully, that is a simple combination of words without statistical model such





1

  

In this evaluation, we did not treat the medium-scale DSSR because it is a recognizer for anaphora resolution.
The best F-value on this experiment was 0.924 in the case that thresh =0.20.
3
On the other hand, the proposed method was “ ”.
2 

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There is a snack on the table.
(Tsukue no ue ni okashi ga aruyone.)
I heard that Mr. Kimura said ``I'm getting hungry.’’(Kimura-san ga onaka ga suita to itteita mitai.)Please carry it to the Kimura’ s room.
(Sore wo kimura-san ni motte itte.)

Pronoun### at Kimura’ s room
Thank you.
(Arigato.)
I have a favor to ask.
(Onegai ga arunodakedo.)
I think that there is a canned drink in the refrigerator.
(Tasika, reizouko ni zyu-su ga atta hazu.)
I want to drink it.
(Sore wo nomitai no dakedo.)
Please bring (it) to me.
(Totte kureru?)....

PronounZero pronoun
in Japanese

Figure 5: An example of a dialogue for the anaphora resolution.
Table 1: The accuracy of anaphora resolution.
MethodAccuracy

Baseline
64.2%

Related work
68.9%

Proposed
71.7%

as word n-grams. We need to consider the vocabulary and grammar files or the language model ofthe medium-scale DSSR to improve the accuracy.
In our method, we handle the change of the situation in a dialogue. It is, however, the change
of a speaker and the location only. To improve the accuracy of the anaphora resolution, we needto incorporate more detailed situation change model such as topics in the dialogue.

4.3 Related work
For the utterance verification task, many approaches have been proposed. Sako et al. (2006) havereported a method to discriminate a request to a system from a chat using AdaBoost. Machinelearning techniques generally need a large amount of training data to generate a classifier with highaccuracy. However constructing training data by hand is costly. Isobe et al. (2007) have proposeda multi-domain speech recognition system based on the model likelihoods of the different domain
specific language models. The method needs to recalculate a model to select an output. On theother hand, our method only changes two thresholds.
Komatani et. al. (2007) have reported an utterance verification method based on a differenceof acoustic likelihood values computed from two recognizers. Kumar et. al. (2005) have utilized
Bhattacharyya distance to measure an acoustic similarity of different languages for multilingual
speech recognition. Using the difference of acoustic likelihood is adequate for the verification
task. Combining a method based on acoustic likelihood with our method is one future work.
For the anaphora resolution task, our method was based on a scoring process using the confidence measure, distance and situation changes. In studies for anaphora resolution on text, machinelearning-based methods have been used (Iida et al., 2005; Ng and Cardie, 2002). However ma-

PACLIC 24 Proceedings289chine learning-based methods need to a large amount of training data. The most famous approachfor zero pronouns is the centering theory (Kameyama, 1986). Nariyama (2002) has proposed amethod which is an expansion of the centering approach. Minewaki et al. (2005) have reported
an utterance interpretation method based on the relevance theory. Incorporating the linguisticknowledge into our method is one of the most effective approaches.
The most critical problem of the anaphora resolution in speech understanding is insertion anddeletion errors in dialogue logs, namely the existence of noise words and the lack of the antecedent.
Therefore systems need to improve the word recognition accuracy for the anaphora resolution. As
a solution for the problem, we applied a medium scale speech recognizer to our method. It is in the
category of the ROVER method (Fiscus, 1997). Applying different types of speech recognizers to
our method is one future work.
Another approach for the improvement is to repair recognition errors by users. Since our task
is an interaction with a robot, repairing errors in a conversation by users is an effective approach.
Ogata and Goto (2005) have proposed a speech input interface with a speech-repair function.
A dialog processing with visualization of outputs and utterance generation from a robot is one
interesting approach in our task.

5 Conclusions
In this paper, we described a speech understanding method based on a multiple speech recognizer.
We called it “OGSS model”. The method was combination of one LVCSR and several DSSRs.
By using this method, we realized a flexible and robust speech understanding method.
In this paper, we evaluated two processes of the method: (1) output selection and (2) anaphoraresolution. For the output selection, the method was based on the edit distance between each
output. In the experiment, we obtained high F-value (more than 0.9). This result shows that our
method is simple and robust. For the anaphora resolution, the method was based on a scoringprocess of each word with a confidence value in dialogue logs. We also used the distance between
an anaphora expression and an antecedent, and change of situation such as speaker’s change forthe scoring process. Although the proposed method was effective as compared with a baseline, theaccuracy was not high (71.7%). The reason why the accuracy of the anaphora resolution was low
was that the accuracies of the LVCSR and the medium-scale DSSR were insufficient. To improve
the accuracy of the anaphora resolution, we need speech recognizers with more high accuracy for
capturing content words in non-order utterances. One approach to solve the problem is to apply astatistical model to the medium-scale DSSR.
Our future work includes (1) a large-scale experiment especially the anaphora resolution, (2)evaluation of the proposed method with other domains and (3) improvement of the accuracy of themedium-scale DSSR.

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