2-8-5
Analysis of Emphasis on Japanese-English Bilingual Corpora ∗◎ Do Quoc Truong, Sakriani Sakti, Graham Neubig,
Tomoki Toda, Satoshi Nakamura (NAIST)

1

Introduction

Table 1

Conventional speech-to-speech (S2S) translation[1] systems only translate the content of the utterance and ignore paralinguistic information includedin the input speech. As a step towards addressing

Utterances

1015

Emphasized units

1305

Utterances has X emphasizedunits

this limitation, in this paper we analyze paralinguistic information across languages. Among the various types of paralinguistic information, we focus
on emphasis, a type of information that is used toconvey the focus of the sentence. Emphasis is an

The conversation corpus materials

Table 2123776
193
4145

Recorded speech data for the conversation

corpusimportant factor especially when repeating an initially misheard sentence (a situation that occurs often when using less-than perfect speech translationsystems). This paper describes an analysis regarding how emphasis is expressed across two languages:

Speaker

Utterances

Male Japanese

1015

Emphasizedwords
1305

Male American

1015

1305

Japanese and English.

32

We did an analysis of the recorded data to see thediﬀerences between Japanese and English in terms

Construction of a corpus of emphasized speech

Analysis

The construction of this corpus is described in thefollow procedure:of amplitude, duration and changes in fundamental frequency (F0). All recorded data were used forevaluation.

First, we selected 16k pairs of sentences from theBasic Travel Expression Corpus (BTEC) [2] whichFirst, taking a look at amplitude in Figure 1
we can see that the amplitude of Japanese empha-consists of sentences from travel domain, and performed part-of-speech tagging on both languages.
We used the NLTK [3] toolkit for English sentences

sized words are almost equal to English emphasizedwords, the diﬀerent between median of amplitutedistribution of emphasized words and normal words

and Mecab [4] for Japanese sentences.
Next, we performed word alignment between theis 10 dB in English and 9 dB in Japanese. However, the Figure 2 shows that English speakers tend

sentences using the pialign tool [5] to ensure that thesame words were analyzed in both sentences. Thissetting allows us to analyze how emphasis can beto emphasize words by using more duration thanJapanese speakers. The median of duration for emphasized words is 400 ms higher than normal words

translated between languages. After this, we had
2500 sentences. These sentences were veriﬁed man-in English and 250 ms in Japanese. The study in [6]said that Japanese emphasis uses less power and du-ually to ensure the correctness and naturalness ofemphasized units. After manual veriﬁcation, a totalof 1015 pairs of sentences remained, the details ofration than English. This is consistent with our duration’s observation, but inconsistent regarding amplitude’s observation. The reason is in our record-which are shown in Table 1.
There were 2 speakers, one Japanese and the othering conditions, the speakers were asked to speak theJapanese sentences right after English sentences, soAmerican. They can speak both Japanese and English ﬂuently. The speakers were asked to emphasize
some particular words intentionally.

the Japanese emphasized words may have been inﬂuenced by the English emphasized words.
Figure 3 shows the average standard deviation∗

日英対訳コーパスにおける強調音声の分析

日本音響学会講演論文集- 51 -2014年9月

Fig. 1

Amplitude distribution

Fig. 3

Average standard deviation distribution

than in Japanese to emphasize words.

5

Acknowledgements
Part of this work was supported by JSPS KAK-ENHI Grant Number 24240032.

参考文献[1] S. Nakamura, “Overcoming the language barrier with speech translation technology,” Science& Technology Trends - Quarterly Review No.31,
April 2009.
Fig. 2[2] T. Takezawa G. Kikui, E. Sumita and S. Yamamoto, “Creating corpora for speech-to-speech

Duration distributiontranslation,” in Proceedings of EUROSPEECH,
2003, pp. 381–384.

(std) distribution of F0 for a single word, representing the amount of frequency change within words.
In this work, we calculate the standard deviation foremphasized and normal words. The higher average
std, the more ﬂuctuation of F0. We can see fromthe result that the emphasized words have higher
ﬂuctuation of F0 compared to normal words. The
diﬀerent between median of average std distributionof emphasized words and normal words in Japaneseis 14.4 Hz and 14.8 Hz in English.

4Conclusion

This paper described the collection of an emphasized speech corpus and the analysis of emphasis
factor across languages. We found that our anal-[3] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python, O’Reilly Media,
Inc., 1st edition, 2009.
[4] T. Kudo,
MeCab:Yet Another Partof-SpeechandMorphologicalAnalyzer,
http://mecab.sourceforge.jp, 2006.
[5] G. Neubig, T. Watanabe, E. Sumita, S. Mori,and T. Kawahara, “An unsupervised model forjoint phrase alignment and extraction,” in Proceedings of ACL, 2011, pp. 632–641.
[6] Mary E. Beckman and Janet B. Pierrehumbert,

ysis is consistent with the prior knowledge that inEnglish people often use more power and duration

日本音響学会講演論文集- 52 -“Japanese prosodic phrasing and intonation synthesis,” in Proceedings of ACL, 1986, pp. 173–180.

2014年9月