Rythmic Metrics of Korean

7/27/2019 Rythmic Metrics of Korean

1/18

Rhythm Metrics of Spoken Korean*

1)

Tae-Yeoub Jang

(Hankuk University of Foreign Studies)

Tae-Yeoub Jang. 2009. Rhythm metrics of spoken Korean.

Language and Linguistics. 46, 169-186. In this paper, the rhythm

characteristics of spoken Korean are investigated. Instead of

conventional methods in which rhythm events are examined by

researchers' impressionistic judgments and interpretation,

calculation of numerical measures are performed so that a large

amount of data tokens can be processed. Besides, the technique of

automatic speech recognition (ASR) is adopted for the purpose of

future application of measured values into practical systems.Results show that some metrics are useful for characterizing the

rhythm structure of Korean utterances as compared to that of other

languages, especially those traditionally classified as stress-based

language. It is also discussed whether the conventional dichotomy of

rhythm classification is valid and plausible.

Keywords: Korean rhythm, rhythm metrics, syllable-based rhythm, stress-based

rhythm

1. Introduction

Traditional binary distinction of speech rhythm, i.e.,

syllable-timed or stress-timed (Pike 1945, Abercrombie 1967), has

been considerably weakened on the ground of phonetic studies

* This work was supported by Hankuk University of Foreign Studies Research Fund

of 2008.


2/18

170 46

providing experimental evidence against inter-stress orinter-syllable isochrony (Pointon 1980, Dauer 1983, Roach 1995,

among others). The terminology has also become more generalized

to syllable-based and stress-based rhythm (Laver 1994: 528),

which will be adopted in the current study.

Recent studies on speech rhythm tend to be focused on

discovering acoustic characteristics of rhythm that will distinguish

one language from another regardless of whether it is classified aseither stress-based or syllable-based. Such metrics known as %V,

V, C (Ramus et al. 1999), VarcoC, VarcoV (Dellwo 2006, 2008),

nPVI-V, rPVI-C (Grabe and Low 2002) have been found to be

useful for characterizing the rhythmic structure of various spoken

languages.

A number of studies have been investigated through one or more

metrics for various languages, other than English, includingChinese (Lin and Wang 2007), Japanese (Murty et al. 2007),

Thai-Polish (Grabe and Low 2002), Estonian (Grabe and Low 2002,

Asu and Nolan 2005), and Dutch-French-Spanish (White and

Mattys 2007). These studies attempt to show that cross linguistic

comparisons of rhythm structures between various languages are

convenient and valid when variability metrics are employed.

Speech rhythm of Korean has most frequently been categorizedinto the class of language with the syllable-based rhythm. However,

it has often been classified into stress-based (Lee 1982) or even

mora-based (Cho 2004). However, it has not yet been

systematically investigated in terms of various rhythm metrics

which were discovered relatively recently. Consequently, in the

current study, eight metrics (%V, V, C, VarcoC, VarcoV, nPVI-V,

rPVI-C, and Speech rate) are utilized in an attempt to characterize

the rhythm structure of spoken Korean. As it will be hard to


3/18

Rhythm Metrics of Spoken Korean 171

interpret the results alone which are in the form of numericalvalues, they will be compared with corresponding values extracted

from previous studies on rhythm metrics of other languages such as

English, Dutch, Spanish, and French. This will lead to further

discussions and tentative determination of which type of rhythm

better describes the spoken Korean.

The conventional way of rhythm investigation mainly depends

upon the impressionistic judgment and interpretation of speechdata. Even most of the studies trying to characterize speech rhythm

using fairly recent rhythm metrics more often than not adopt the

method of calculating measures in a habitual way of manual data

treatment. A shortcoming of this method is that only a relatively

small amount of data can be examined. On the contrary, in the

current study, a large number of speech tokens will be analyzed in fully

automatical ways so that more reliable and robust results can be derived.These results can be directly applicable to practical systems such as speech

recognition/synthesis machines and pronunciation education tools.

2. Rhythm measures

The basic idea underlying various rhythm metrics is that

stress-based languages have more variable temporal characteristics

among their vocalic and/or consonantal intervals of spoken

utterances than syllable-based languages. The phonological contrast

triggered by stressing or de-stressing of syllables will bring about

such differences in acoustic quality, especially temporal

characteristics, accompanying elongation of stressed syllables or

shortening of unstressed or reduced syllables. Therefore, duration of

syllables will become more variable in stress-based languages than


4/18

172 46

in syllable-based languages. The syllable structure of stress-basedlanguages is also expected to be more complex as assigning stress is

usually related to the weight of the syllables, for instance, by

allowing consonant clusters in the onset and/or coda position of a

syllable. This will generate greater variability among consonantal

intervals.

Eight rhythm metrics that are most frequently investigated in

many languages have been employed in the current study. Thenotion of each metric can be summarized as in (1).

(1) a. %V: proportion of vowel intervals (Ramus et al. 1999)

b. V: raw variation of vowel intervals (Ramus et al. 1999)

c. C: raw variation of consonantal intervals (Ramus et al.

1999)

d. VarcoC: rate-normalized variability of consonantal intervals

(Dellwo 2006)e. VarcoV: rate-normalized variability of vocalic intervals

(Dellwo 2006, White and Mattys 2007)

f. nPVI-V: normalised Pairwise Variability Index (Grabe and

Low 2002)

g. rPVI-C: raw Pair-wise Variability Index (Grabe and Low

2002)

h. Speech rate: number of syllables per second

The metric %V is calculated by dividing the total duration of

vowel intervals by the total duration of the utterance. Utterance

internal pauses of non-speech parts, which are frequently found in

non-native speech, are to be excluded in the current study. The

measures V and C are obtained by calculating standard

deviations of vowel and consonant intervals, respectively. The VarcoV and

VarcoC are rate-normalised versions of V and C. They are derived by

dividing raw values by the mean duration of the utterance. The PVI values

represent variations of adjacent intervals of vowels (nPVI-V) and consonants


5/18


(rPVI-C). Using these pair-wise indices are expected to capture thecomplexities of neighboring syllables. The last metric, speech rate, has been

included in order to check whether the other metrics play a sustained role as

rhythm type discriminator regardless of articulation rate of utterances as well

as to examine the characteristics of normal speech rate of Korean in

comparison to other languages.

The basic expectation is that values of variability metrics (bto g,

in (1) above) of Korean, if categorized as a syllable-based language,will be less than those of English or other stress-based languages,

presumably due to lack of duration-sensitive prosodic events such

as stress and pitch accents. On the other hand, %V is expected to

be greater in Korean than that of stress-based languages

considering that relatively complicated syllable-internal consonant

clusters (e.g., three consecutive consonants at the onset position or

four at the coda position in English syllables) are not phoneticallypermissible in Korean. Finally, the speech rate, measured by the

number of syllables per unit time, of Korean utterances are

expected to be closer to that of other syllable-based languages such

as Spanish or French than stress-based languages, assuming that

syllables of syllable-based languages in general comprise a smaller

number of segments than those of stress-based languages. These hypotheses

will be tested in the experiment.

3. Data and methods

Speech data tokens are extracted from a large database named A

read speech corpus of Seoul Korean (2003) developed and released

by The National Institute of the Korean Language.1) After

1) The corpus is publically available at http://www.korean.go.kr.


6/18

174 46

sentence-unit tokenization and other necessary preprocessing,phonetic annotation is performed to generate detailed phone-level

boundary information. Given the size of data, automatic

segmentation and labeling techniques are employed to alleviate an

excessively large amount of time and efforts for manual annotation.

3.1. Data

Assuming no rhythm variation by speakers' age, I pick 40

speakers whose age varies from 20's to 40's: 10 males and 10

females in their 20's, 10 males in their 30's and 10 females in their

40's. The recording prompt of the corpus, according to the corpus

documentation, is composed of passages from 19 Korean fairy tales

and short novels. The number of sentences for each speaker to read

is 930 and there is a total of 36,410 speech tokens used in the

current analysis.2)

Speech data files are preserved in the form of digital waveform files with

the 16-bit quantization and 16 KHz sampling rate.

3.2. Automatic segmentation

As the rhythm metrics calculation is based on temporal

information of each phone in the speech tokens, phone-level

segmentation and annotation is the most important procedure prior

to metrics calculation. As is mostly the case, this is achieved by

constructing a phone-level automatic speech recognizer, whose

2) The total number should be 37,200 (930 sentences x 40 speakers), but 790tokens are excluded: erroneous files caused by defective recording, files with too

short sentences (e.g., a-ni-o) from which the extraction of utterance-level

rhythm structure is considered to be inappropriate.


7/18


implementation is summarised in Table 1.

Training data KAIST data (Park et al. 1995) with 10863

read speech tokens by 89 Korean speakers

Units 34 phoneme-like units including silence

Modeling 3-state continuous left-to-right Hidden

Markov Models

Features

39 dimensional feature vectors:12 MFCC + 1 energy + 13 deltas + 13

delta-deltas

System enhancement

unterance internal pause/silence modeling

dictionary expansion through pronunciation

variation modelling

Automatic phone recognizer specification

Automatic segmentation is conducted by the tool named Hidden

Markov Model Tool Kit(HTK) version 3.2 (Young et al. 2003).Figure 1 is an example of autolabels compared with the

corresponding labels produced by hand.

Comparison between autolable (upper tier) and handlabel (lower

tier): labels of a single utterance "tuk wi-lo ol-la seoss-ta". This autolable file

has been randomly selected from the whole set of autolabels and the

corresponding handlabling is performed by myself without referring to the

ready-performed autolabels.

As illustrated, the two labeling methods are quite correlated. It

has been verified that over 94% of autolabels are less-than-10-msec apart


8/18

176 46

from their corresponding handlabels. It does not necessarily mean thatautolabeler is responsible for the 6%-mismatch. In fact, errors caused by

hand tend to be more frequent and unpredictable that it is premature to

determine which method is more reliable, apart from the time required. As a

consequence, it is assumed, in the current analysis, that autolabeling does not

significantly undermine the quality of measurement.

3.3. Calculation of metrics

Once phonetic labels are provided, all the rhythm metrics are

calculated, again automatically, to generate an overall mean value

for each metric by averaging a set of values obtained from each

utterance token. After metrics are calculated for each token, an

overall value for each of eight metrics is generated by averaging the

corresponding metric values.

A program is created to enable this task to be performed fully

automatically, as shown in Figure 2.

Metrics calculation procedure

3.4. Cross-linguistic comparison

Interpretation of metrics is conducted by comparing their values with


9/18


corresponding metric values of English, Dutch, Spanish and French that arereported in White and Mattys (2007). Although there are other studies

measuring rhythm metrics of various languages, it is not appropriate to

employ their results for comparison since the methods used are not uniform

as they occasionally modify the formula of rhythm metrics for the purpose of

language specific adaptation.

However, validity of the present comparison is still restricted

mainly because the data used in the current experiment and thedata of the other languages in White and Mattys (2007) are not

homogeneous. No more than 30 tokens are used for each of four

languages in their experimentation while a much greater number of

data tokens are currently being analyzed for Korean. Inevitably, the

standard deviation of each metric value for Korean will be a lot

greater than that for other languages. Certainly, it would have been

a more parallel comparison if the metric values for other languagesare calculated from a larger database, but this difference does not

make it inappropriate to compare those results as the algorithm for

each rhythm metric has been consistent in both studies.

Nevertheless, due to the difference in data size, the cross-linguistic

comparison is to be performed without statistical significance tests.

4. Experimental results

Table 2 is the metric values of Korean and four other languages.

Each value is the mean (and standard deviation) of values averaged

over all tokens.


10/18

178 46

Syllable-based ? Stress-based

Spanish French Korean English Dutch

%V 48 (0.8) 45 (0.5) 54 (6.9) 38 (0.5) 41 (1.2)

V 32 (1.9) 44 (2.2) 64 (22.5) 49 (2.2) 49 (2.6)

C 40 (2.3) 51 (3.6) 49 (18.3) 59 (2.4) 49 (4.1)

VarcoV 41 (2.0) 50 (0.9) 64 (14.0) 64 (1.7) 65 (1.5)

VarcoC 46 (2.0) 44 (0.8) 59 (14.0) 47 (1.0) 44 (1.8)

nPVI-V 36 (1.6) 50 (1.8) 61 (13.2) 73 (1.2) 82 (2.4)

rPVI-C 43 (2.1) 56 (4.3) 55 (18.6) 70 (2.8) 52 (4.2)

Speech Rate

(syl/sec)8.0 (0.3) 5.6 (0.3) 6.4 (0.9) 5.2 (0.2) 6.0 (0.3)

Mean value (and their standard deviation) of rhythm metrics of Korean ascompared to Spanish, French and English. Values of languages other than Korean

are based on White and Mattys (2007).

Results show that the metrics %V and nPVI-V seem to verify the

hypothesis that the Korean speech rhythm is syllable-based.

Especially, the %V value of Korean is greater than that of all the

other languages considered. It indicates that Korean utterances are

composed of relatively a larger portion of vocalic intervals than

English utterances, conforming to the notion that a language closer

to the ideal syllable-based language enforces its syllables to contain

relatively less complex consonantal clusters than languages with the

ideal stress-based rhythm.

Unexpectedly, VarcoV of Korean is closer to stress-based

languages and V, C and rPVI-C do not seem to give useful

information on classification, either. On the other hand, the speech

rate of Korean seems, at a glance, to be meaningful as it is greaterthan English and smaller than Spanish. However, further

investigations are necessary to confirm its crucial role in


11/18


characterizing speech rhythm, as other various factors includingspeaker style, text type, recording environment are believed to

affect the speech rate more or less. Thus, it is premature to regard

speech rate as critical cue to distinction between syllable-based and

stress-based rhythm.

All in all, on the assumption that Korean has more a

syllable-based rhythm than stress-based rhythm, the two metrics

%V and nPVI-V are helpful for describing its rhythm structure.Consequently, the rhythm structures of Korean and the other four

languages are represented in Figure 3 in terms of those two rhythm

measures.

Rhythm structure of five languages (Korean, Spanish, French, Dutch and

English) based on rhythm metrics with respect to vocalic intervals: each of the

coordinate values represents (nPVI-V: %V), respectively.

At a glance, based on the imaginary separation line, Korean

appears to be classified as a syllable-based language together with


12/18

180 46

Spanish and French. This observation, however, can be somewhatillusory when we consider each of the two metrics independent of

the other. While %V locate Korean uppermost resulting in farther

distance from English and Dutch, the other metric nPVI-V put

Korean somewhere in the middle on a continuum between two

extreme types of rhythm. This implies that those two measures are

not sufficient to define the rhythm class of Korean, although it is

more likely that spoken Korean bears a fairly different rhythmstructure from conventionally classified syllable-base languages.

Before concluding, on the basis of these two metrics, that the

rhythm dichotomy is misleading or that characterization of rhythm

varies depending on individual rhythm metrics, it is necessary to

check if those metrics are seriously affected by another factor. The

most suspicious factor is speech rateas it has been argued by other

studies that such metrics as V and C are susceptible to the rateof speech (Barry et al. 2003, Dellwo and Wagner 2003). If values

%V and nPVI-V are also found to change significantly in accordance

with variation of speech rate, they should not be considered to play

an important role of characterizing rhythm as they might be

vulnerable to other factors.

In order to perform this verification, 100 slow speech tokens (4-5

syl/sec) and 100 fast (6-7 syl/sec) speech tokens are randomlypicked and the metrics for each token are calculated. Then, the

significance of difference between metrics from the data with two different

speech rates is assessed through the two-tailed t-tests. The result is shown

in Table 3.


13/18


Slow

(4-5 syl/sec)

Fast

(6-7 syl/sec)p value

No. of Tokens 100 100

V 55.1 54.7 p


14/18

182 46

of prosody processing systems and pronunciation education tools.Based on the metrics utilized in the current experiment, Korean

seems to be categorized as a language with the syllable-based

rhythm structure, but not as distinct from stress-based language as

Spanish or French. More comprehensive comparisons with metrics

extracted from other languages are expected to give further clues to

discover whether it makes better sense to describe speech rhythm in relative

terms on a continuum between the two stereotypes of speech rhythm, i.e.,extremely syllable-timedand extremely stress-timed, instead of defining on

the traditional stronger version of binary classification. Still another

possibility, not yet obvious, is that even weak version of dichotomy is not

appropriate as individual metrics can categorize each language in quite

different manners. To come closer to the clarification, more languages need to

be tested in terms of other rhythm measures that have been employed in

previous research, or that are still to be discovered in the future.


15/18


References

Abercrombie, D. (1967) Elements of General Phonetics. Edinburgh:

Edinburgh University Press.

Asu, E. L. and F. Nolan (2005) Estonian rhythm and the Pairwise

Variability Index. In Proceedings, Fonetik 2005 29-32.

Gteborg University, Sweden.

Barry, W. J., B. Andreeva, M. Russo, S. Dimitrova and T.Kostadinova (2003) Do rhythm measures tell us anything

about language type? In Proceedings of the 15th

International Congress of Phonetics Sciences 2693-2696.

Barcelona.

Cho, Moon-Hwan. (2004) Rhythm typology of Korean speech.

Cognitive Process5: 249-253..

Dauer, R. M. (1983) Stress-timing and syllable-timing reanalyzed.Journal of Phonetics11: 51-62.

Dellwo, V. (2006) Rhythm and speech rate: A variation coefficient

for delta C. In P. Karnowski and I. Szigeti (eds.), Language

and Language Processing: Proceedings of the 38th

Linguistic Colloquium231-241, Piliscsaba 2003. Frankfurt:

Peter Lang.

Dellwo, V. (2008) The role of speech rate in perceiving speech

rhythm. In Speech Prosody 2008, Proceedings of Speech

Prosody 2008 series. Campinas, 375-378.

Dellwo, V. and Wagner, P. (2003). Relations between language

rhythm and speech rate. In Proceedings of the 15th

International Congress of Phonetics Sciences 471-474.

Barcelona.

Grabe, E. and E. L. Low (2002) Durational variability in speech

and the rhythm class hypothesis. Papers in Laboratory


16/18

184 46

Phonology7: 515-546. Berlin: Mouton.

Laver, J. (1994) Principles of Phonetics. Cambridge: Cambridge

University Press.

Lee, Hyun-Bok. (1982) A phonetic study on Korean rhythm.

Malsori [Speech Sounds] 4: 31-48. The Korean Society of

Phonetic Sciences and Speech Technology (in Korean).

Lin, Hua and Qian Wang (2007) Mandarin rhythm: An acoustic

study. Journal of Chinese Linguistics and Computing17(3): 127-140.

Murty, L., T. Otake and Anne Cutler (2007) Perceptual tests of

rhythmic similarity: I. mora rhythm. Language and Speech

50(1): 77-99.

Nazzi, T., J. Bertoncini and J. Mehler (1998) Language

discrimination by newborns: toward an understanding of the

role of rhythm.Journal of Experimental Psychology24(3):756-766.

Park, J., O. Kwon, D. Kim, I. Choi, H. Jeong and C. Un (1995)

Speech data collection for Korean speech recognition. The

Journal of the Acoustic Society of Korea14(4): 7481. (in

Korean).

Pike, K. (1945) The Intonation of American English. Ann Arbor:

University of Michigan Press.G. Pointon. (1980) Is Spanish really syllable-timed? Journal of

Phonetics8: 293-305.

Ramus, F., M. Nespor and J. Mehler (1999) Correlates of linguistic

rhythm in the speech signal. Cognition73: 265-292.

Roach, P. (1982) On the distinction between stress-timed and

syllable-timed languages. In D. Crystal (ed.), Linguistic

Controversies73-79. London: Edward Arnold.

Young, S., G. Evermann, D. Kershaw, G. Moore, J. Odell, D.


17/18


Ollason, D. Povey, V. Valtchev and P. Woodland (2003)HTK Book (for version 3.2). Cambridge University Press.

White, L. and S. L. Mattys (2007) Calibrating rhythm: first

language and second language studies. Journal of Phonetics

35: 501-522.

Department of English Linguistics

Hankuk University of Foreign Studies

270 Imun-dong, Dongdaemun-gu, Seoul 130-791, Korea

e-mail: [email protected]

Received: Aug. 31, 2009

Revised: Oct. 15, 2009

Accepted: Oct. 18, 2009


18/18

Rythmic Metrics of Korean

Documents