language and speech, A.
2009,
Neijt,
52R.(1),
Schreuder79–112

79

Japanese Mental Syllabary andEffects of Mora, Syllable, Bi-mora
and Word Frequencies on Japanese
Speech Production
Katsuo Tamaoka1, Shogo Makioka2
1 Nagoya University, Nagoya, Japan

2 Osaka Prefecture University, Sakai, Japan

Key words

Abstract

The present study investigated the existence of a Japanese mental syllabaryand units stored therein for speech production. Experiment 1 comparednaming latencies between high and low initial mora frequencies using
Japanese
CVCVCV nonwords, indicating that nonwords with a high initial moralanguagefrequency were named faster than those with a low frequency initial mora.
Experiments 2 and 3 clarified the possibility of CV light and CVN/CVR
light and heavy
heavy syllables as being units implicated in speech production. CVNCV
syllables
nonwords in Experiment 2 and CVRCV nonwords in Experiment 3 displayed
shorter naming latencies and lower error rates than their baseline (samemental syllabarybi-mora frequencies) of CVCVCV-structured nonwords. Since bi-morafrequencies between CVN/CVR and CVCV were the same, heavy syllables
mora frequency
comprised of CVN and CVR units may contribute to ready-made motorprograms stored in the Japanese mental syllabary as variations of the 100
word frequency
core light syllables (300 units in total). Experiment 4 further tested theeffects of bi-mora frequency on the naming of nonwords, and found that CVCVCV-structured
nonwords with high bi-mora frequencies were named more quickly and accurately than those withlow bi-mora frequencies, although some bi-mora combinations seem to exhibit nonconforming
tendencies (i.e., null significance in item analysis). Experiment 5 demonstrated that the namingof real words with high word frequency was quicker than for other real word conditions with low
word frequencies (i.e., word frequency effects), with little effect of bi-mora frequencies. Unlike
the nonword condition of Experiment 4, bi-mora frequency had only a minor influence on thenaming of real words. Based on these findings, the present study proposes a possible model of theJapanese mental syllabary accompanied by a discussion of bi-mora and word frequency effects.
bi-mora frequency

1 Introduction

Single Japanese words like “family” /kazoku/ can be phonologically classified into
three different units: a lexical unit of /kazoku/, bi-mora units of /kazo/ and /zoku/, and			Address for correspondence. Katsuo Tamaoka, Graduate School of Languages and Cultures,
Furou-cho, Chikusa-ku Nagoya University, 464-8601, Japan; <ktamaoka@gc4.so-net.ne.jp>

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80Japanese mental syllabarymora units of /ka/, /zo/, and /ku/. It is a well-known fact that word frequency affects
phonological processing (e.g., Forster & Chambers, 1973; Rubenstein, Garfield, &Milllikan, 1970; Taft, 1979, 1991; Whaley, 1978, for word frequency effect in English;
Fushimi, Ijuin, Patterson, & Tatsumi, 1999; Hino & Lupker, 1998; Tamaoka &Takahashi, 1999, for word frequency effect in Japanese) in the way that the thresholds
of frequently-seen words is lower, making activation of phonological representations
easier. However, it has not yet been conclusively demonstrated whether smaller nonlexical units of mora, syllable, and bi-mora frequencies affect Japanese phonologicalprocessing independent of word frequency. Thus, the present study conducted fiveexperiments using a naming task for Japanese words and nonwords to investigate thefour factors of mora, syllable, bi-mora, and word frequencies.

2 The unit of Japanese speech production

Previous studies (e.g., Cutler & Otake, 1994; Kubozono, 1985, 1989, 1995; Otake,
Hatano, Cutler, & Mehler, 1993; Otake, Hatano, & Yoneyama, 1996) reported thatnative Japanese speakers use moraic units for auditory perception and speech segmentation. This claim seems reasonable when certain examples of Japanese script, culture
and children’s play are considered.
The most common observational intuition is that the Japanese phonetic script
kana represents moraic units, so that the unit of mora should be used for Japaneseorthographic and phonological processing. From a cultural perspective, it is a popular
notion that since the well-known Japanese haiku is composed on the basis of 17 morae(i.e., three phrases of 5–7–5 morae), the mora unit is most likely to be the unit forJapanese phonetic counting. From a developmental perspective, through the phonetic
game shiritori, a word game requiring the segmentation of beginning and ending
morae, Japanese children become aware of morae as important units for Japanese
speech segmentation and phonetic assembly. As such, observations based on selected
examples of Japanese script, poetic verse and children’s play suggest the possibilityof the mora as a Japanese phonetic unit.
However, as shown in Figure 1, recent studies in Japanese phonology (e.g.,
Haraguchi, 1996; Kubozono, 1989, 1995, 1999; Kubozono & Ota, 1998; Terao, 2002)
propose that morae and syllables may coexist in a single hierarchical structure.
In this structure, the lowest phonological level is the phonemic level, representing
consonants, semivowels, and vowels. The next higher level is the moraic level. The
first mora (µ1) is constructed from a consonant (C), a semivowel (S; more preciselyreferring to the semivowels /y/ and /w/), a vowel (V) and empty (Ø), and the second
mora (µ1) from a consonant (i.e., a moraic nasal /N/ or a moraic geminate /Q/) or avowel (i.e., a long vowel /R/ or a dual vowel /J/). The highest level in the figure is thesyllabic level. Units at this level are constructed from C(S)V and C/V, which create
CVC and CVV syllables, with the occasional single V or SV structure with an empty
consonant (i.e., ØV and ØSV).
Japanese sounds are usually represented by a CV structure including ØV andØSV which consist of one mora, while all the four special sounds (i.e., moraic nasal/N/, geminate /Q/, long vowel /R/ and dual vowel /J/), which form a CVC or a CVV
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K. Tamaoka, S. Makioka

81

Figure 1
Fundamental structure of Japanese phonemes, morae, and syllables.
σ(Syllable)µ1(Mora)C, CS, S, ∅Syllabic levelµ2(Mora)V

C/V/N/, /Q/, /R/, /J/

Moraic level

Phonemic level

Note: A combination of C (consonant) and S (semivowel) is considered as one phonemic unit. “∅;” refers
to an empty phonemic unit.

­ honological structure, are a single syllable. If a CV-structured single mora is regardedp
as a light syllable, the CVC/CVV structure can be understood as unit comprising aheavy syllable. Based on this syllabic concept, then, it is quite possible that all Japanesemorae can be interpreted as syllabic units.
Tamaoka and Terao (2004) conducted two experiments to investigate the unit fornaming visually presented stimuli, focusing on the special sounds /N/, /Q/, /R/ and/J/. These special sounds create two morae when there is only one syllable. Theirexperiments compared the production of three-mora three-syllable (i.e., CVCVCV)and three-mora two-syllable (i.e., CVNCV, CVRCV, CVQCV and CVJCV) nonsensewords. Findings indicated that native Japanese speakers named the three-mora
nonwords containing the special sounds more quickly than those of the former group.
Accordingly, we posited that special sounds would be named as syllables rather thanmorae. The computational load of Japanese speech production could be greatly
decreased by assembling sounds at the syllabic level in Figure 1. Consequently, in
addition to evidence provided in Tamaoka and Terao (2004), the present study also
investigated whether or not syllables (as opposed to morae) are used for speech
production by controlling bi-mora frequencies.

Type and token frequencies of morae, syllables,
3 andbi-morae
There are two ways to represent “frequency”: type and token. Type frequency counts
a word only once, whereas token frequency simply represents the number of times atarget item appears. Should the item in question be a word, the token frequency isalso known as word frequency. Amano and Kondo (2000, and 2003 for the CD-ROM
version) established a lexical database from editions of the Asahi Newspaper printed
from 1985 to 1998, containing a total type frequency of 341,771 morphemic units anda total token frequency of 287,792,797 morphemic units. According to this database,the four-mora word “economic” /ke i za i/ appears 130,052 times, the token frequencyof the word. In contrast, the value of type frequency for this word remains as 1. Morafrequency refers to how often each mora appears in Japanese texts. The mora /ke/ in
Language and Speech

82Japanese mental syllabary/keizai/ is counted as 1 for type frequency while the token frequency for /ke/ in theword is 130,052 and the mora /i/ is counted as 2 for type frequency while the token
frequency for /i/ in this word doubles to become 260,104.
In that all Japanese words can be presented in kana, using the lexical databaseof Amano and Kondo (2000, 2003), Tamaoka and Makioka (2004) created an index ofmora frequencies by counting how often each mora appears in Japanese texts. Since
token frequency represents how often morae, bi-morae, and words are encounteredin real life, the present study used this as an index to investigate the frequency effects
of morae, syllables, bi-morae, and words on Japanese phonological processing. It
should, however, be noted that the lexical database of Amano and Kondo is basedon the written form used in a newspaper, not the spoken form of daily conversation.
Nevertheless, this lexical frequency index is the largest collection of Japanese lexical
items currently available, and the only existing speech corpora in Japanese are yet
too limited in size to be used for our purpose of determining frequency counts. The
use of such corpora would only serve to skew moraic distribution as a result of being
biased on a limited number and nature of both words and topics. Thus, the present
study utilized the lexical database of written form provided by Amano and Kondo(2000, 2003).
Bi-mora frequency refers to how often two morae are combined in Japanese, andis calculated on the basis of kana combinations using the same lexical database. For
example, the frequency of the combination of the morae, /hi/ and /to/ (in this order)
appears as a lexical unit (or word meaning “human”) 229,773 times, whereas the samecombination appears 615,869 times as a part of words (e.g., counting appearances of/hitode/ meaning “a starfish” and /hitomi/ meaning “pupils of one’s eyes”), including
its combination in the single word “human” /hito/. Syllable frequencies of CVR, CVJ,
CVN and CVQ depicted in Figure 1 are possible to count as bi-mora frequencies ofCV plus R, J, N or Q. Since the lexical database of Amano and Kondo (2000, 2003)
stores word frequency counts as lexical units, bi-mora frequencies in the present study
refer to those used in Tamaoka and Makioka (2004), which are limited to within-wordboundaries; cross-word boundaries were therefore excluded.
3.1
Mora frequencies

As shown in Table 1, the top 20 mora frequencies were ranked based on token frequencies taken from Appendixes A and B of Tamaoka and Makioka (2004). The token
frequency for the most frequently-used mora, the vowel /i/, was 43,985,426. The sound/i/ is a single phoneme, but it is also regarded as a single mora constructed by a
combination of an empty consonant and a single vowel (i.e., Ø+V structure). The
second most commonly used mora was /u/, one of the five vowels which is also thesame Ø+V structure, being counted 39,052,254 times. The third most common mora
was the nasal /N/, regarded as a single mora. Although the sound /N/ is considered oneof the “special” sounds, this should not be confused as implying “unusual.” Rather,
being counted 35,719,268 times, the /N/ sound is quite common among Japanese morae.
Interestingly, the nasal /N/ never appears in the initial position of Japanese words,
so that it is always part of a CVN structure (C can be empty, represented as VN)
which can be regarded as a single (heavy) syllable. While the commonly-appearing
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83

Table 1
The top 20 single morae frequencies
Single mora frequency#

Kana

Phoneme

Structure

Token freq.

1

i

ØV

43,985,426

2

u

ØV

39,052,254

3

N

N

35,719,268

4

ru

CV

28,899,891

5

ka

CV

20,530,065

6

no

CV

19,326,674

7

ku

CV

17,211,261

8

to

CV

17,102,180

9

si

CV

16,518,016

10

ta

CV

16,442,465

11

ni

CV

14,757,618

12

o

ØV

14,053,377

13

ha

CV

12,594,714

14

ko

CV

12,154,255

15

ga

CV

11,996,376

16

te

CV

11,037,767

17

na

CV

10,498,607

18

da

CV

9,953,427

19

ki

CV

9,926,212

20

tu

CV

9,612,206

C+V-structured moraic sounds comprised 16 of the top 20 morae, they are not included
among the top three.
Both the English and Dutch languages have more than 12,000 different syllables;
however, 500 different syllables comprise about 80% of English and 85% of Dutch(Schiller, Meyer, Baayen, & Levelt, 1996; Schiller, Meyer, & Levelt, 1997) utterances.
Furthermore, a small number of 80 syllables cover 50% of speech in these languages.
Contrary to English and Dutch, there are commonly-used 100 basic mora and 3
Japanese moraic special sounds of /N/, /R/ and /Q/. The massive adoption of alphabetic
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84Japanese mental syllabary

loanwords, especially from English, has resulted in various new sounds used in modern
Japanese. Taking this new trend into consideration, a list of an additional 33 morae(or kana) was officially issued for these loanwords through a cabinet announcementby the Government of Japan (1991); however, these 33 morae are rare in comparison
with mora frequencies of the core 100 morae. As Levelt, Roelofs, and Meyer (1999,
p.32, Figure 15) depicted for English syllables, Figure 2 presents the cumulative
frequency of use for the 136 morae (i.e., 100 core mora, 3 special moraic sounds and 33
additional sounds), ranked according to token frequencies using frequency data takenfrom Tamaoka and Makioka (2004). A remarkable departure from English and Dutch
syllabic frequency data is found with Japanese, in that the top 13 morae encompass
51.12%, the top 33 morae 80.04%, the top 47 morae 90.05%, and the top 75 morae
99.05% out of the total 579,424,250 mora appearances in a Japanese newspaper.
Another great difference between Japanese and English is seen in terms of the
proportion of possible syllables/morae. The 80 major English syllables cover only 0.67%
of total possible syllables (80 divided by 12,000) while the top 13 morae in Japaneseconsist of 9.55% of a total of 136 morae. Therefore, it takes less than 5% of the possible
syllables to cover 80% of English syllables, but 24.26% of the possible morae (33 morae
divided by 136) to cover 80% of all Japanese morae. Consequently, Japanese takes a
far greater percentage of morae to cover the same number of syllables in English.
Figure 2
Cumulative frequency distribution for the 136 morae in Japanese
100%
90%
80%

Cumulative frequency

70%

60%
50%
40%
30%
20%
10%
0%

1

10

19

28

37

46

55

64 73 82Mora ranking

Note: Data taken from Tamaoka and Makioka (2004).

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91 100 109 118 127 136

K. Tamaoka, S. Makioka

85

Since a smaller proportion of syllables were repeatedly used in Dutch and English,
Levelt and his colleagues (Levelt, 1999, 2001; Levelt et al., 1999) suggested the existence of an independent mental storage consisting of a small number of ready-made,syllabic motor-programs. Within the theoretical framework of lexical access in speech
production, syllables act at the interface of phonological and phonetic encoding.
Abstract phonological syllables, which provide pre-compiled gestural scores for thearticulators, are converted into phonetic syllables, rather than motor syllables online.
Since stored representations corresponding to syllabic units are retrieved from themental syllabary, the retrieval process should be sensitive to their frequencies. Basedon this hypothesis, Levelt and Wheeldon (1994) tested their hypothesis of the mental
syllabary by comparing access latencies of high- and low-frequency syllables controlling for word frequency, finding that words with high-frequency syllables were named
faster than those with low-frequency syllables. This observation supported the notionof the mental syllabary. Further, recent studies report syllable frequently effects on
speech production in Spanish (Carreiras & Perea, 2004; Stenneken, Conrad, & Jacobs,
2007), French (Brand, Rey, Peereman, & Spieler, 2002), and Dutch (Cholin, Levelt, &Schiller, 2006). Since the Japanese language has a limited number of easily-identifiablesyllabic (or moraic) units, these findings imply that a similar mental syllabary also
exists in the Japanese language.
3.2
Syllable and bi-mora frequencies

As shown in Table 2, the most frequently-used bi-mora was the combination of /ka/and /i/, having been counted 4,269,940 times. Similarly, the second most commonlyused bi-mora /te/+/i/ represents a combination in which the first CV is followed by
the vowel /i/. The third most commonly used bi-mora /ko/+/u/ appears very often as aunit /koR/. The second mora /u/ becomes the sound /o/ which most frequency results
in a long vowel /R/. The fourth most frequently occurring bi-mora, /se/+/i/, reflects
the same pattern as the first and the second cases. All these combinations of the top
four bi-morae are often regarded as either two moraic units or a single syllabic unit.
Surprisingly, 15 of the top 20 bi-morae fall into the category of CVC or CVV syllabic
structures as depicted in Figure 1 (e.g., Haraguchi, 1996; Kubozono, 1989, 1995, 1998,
1999; Kubozono & Ota, 1998; Terao, 2002). Thus, it is interesting to find that syllabic
units are commonly seen in Japanese bi-mora structures. As previously mentioned,
since CVV/CVC-structured syllabic units occur only with the four special sounds
which cover about 8–9% of Japanese morae, it may be more suitable to consider theunits of the mental syllabary in the Japanese language as light/heavy syllables, rather
than morae.
3.3
Outline of five experiments

The present study consisted of five experiments to separately investigate the complex
effects of mora, syllable, bi-mora, and word frequencies.
Experiment 1 was conducted to prove the effects of mora frequency using thenaming task of CVCVCV-structured nonwords comprised of high- and low-frequency
items for the initial CV mora. If nonwords with a high-frequency initial mora were
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86Japanese mental syllabary
Table 2The top 20 single bi-morae frequencies
Bi-mora frequency#

Kana

Phoneme

Structure

Token freq.

1ka + iCVV4,269,940

2

te + iCVV

3,851,691

3

ko + uCVV3,669,655

4

se + iCVV

3,661,544

5ka + NCVN

3,044,363

6

i + ruØVCV3,016,280

7

su + ru

CVCV

2,935,841

8

to + uCVV2,708,538

9na + iCVV

2,653,620

10

re + ru

CVCV

2,643,258

11ta + iCVV2,632,62312

u + siØVCV2,282,422

13

ke + NCVN

2,221,991

14

da + iCVV2,120,498

15

se + NCVN

2,106,924

16

yo + uCVV

1,999,20817

do + uCVV

1,930,21718

si + NCVN

1,915,000

19

ko + ku

CVCV

1,851,379

20

sa + NCVN

1,764,878

named faster than those with the low-frequency initial mora, it would support theidea of the mental syllabary (Levelt et al., 1999) at the moraic level. We therefore
extended the processing unit from mora to syllable through two additional experiments, Experiments 2 and 3.
Since the special sound /N/ can be attached to any basic mora unit (ØV, CV, ØSV,CSV), the present study hypothesized that the syllabic CVN unit (as equal to the CVunit) could be the processing unit for Japanese speech production. Once the moraic
units of ØV, CV, ØSV and CSV are regarded as light syllables and the syllabic units of+N and +R as heavy syllables, units in the Japanese language for speech production
can be considered as syllables, not morae. Thus, Experiment 2 compared the naming
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K. Tamaoka, S. Makioka

87

latencies of CVNCV (three morae or two syllables) and CVCVCV (three morae or threesyllables) nonwords, controlling for the bi-mora frequencies of these two conditions(CV+N and N+CV for the CVNCV condition, and the initial bi-mora and secondary
bi-mora combinations for the CVCVCV condition).
One other special sound, /R/, can also be attached to any basic mora unit. Thus,
Experiment 3 compared naming latencies between the CVRCV-structured nonwords(three morae or two syllables) and the CVCVCV-structured nonwords (three morae or
three syllables), again by controlling for bi-mora frequencies. If both Experiments 2
and 3 would show that three-mora CVNCV and CVRCV have shorter naming latencies
than their counter nonwords of CVCVCV, two types of heavy syllables, CVN and
CVR could be a part of the Japanese mental syllabary produced as two variations
from the core 100 basic morae or light syllables.
Independent of mora frequency effects, Experiment 4 compared the naming
latencies of three-mora CVCVCV-structured nonwords with high bi-mora frequencies and low bi-mora frequencies using identical moraic units with different bi-mora
combinations. The high-frequency bi-morae would likely be activated more quickly
and accurately than less frequently-used bi-morae.
Finally, Experiment 5 compared naming latencies for three-mora CVCVCVstructured real words (1) with high word and high bi-mora frequencies, (2) with highword and low bi-mora frequencies, and (3) with low word and low bi-mora frequencies.
This experiment expected that the effects of mora and bi-mora frequencies influence
the speed of speech production (or shorter naming latencies) independent of the lexical
nature of word frequency.

Experiment 1: Comparison of CVCVCV-structured
4 nonwords
with high and low frequency initial mora
Experiment 1 was conducted to compare naming latencies and error rates of nonwords
between the high and low initial mora (CV1) frequency using CV1 CV2 CV3 -structured
nonwords.
4.1
Method
4.1.1
Participants

Twenty-eight graduate and undergraduate students (12 females and 16 males) at
Hiroshima University in Japan, all native speakers of Japanese, participated inExperiment 1. Ages ranged from 19 years and 1 month to 24 years and 5 months, withthe average age being 20 years and 8 months on the day of Experiment 1.
4.1.2
Stimuli

Nonwords in the present study were defined as items which did not exist in a large
lexical database of 341,771 morphemes (type frequency) established from newspapers
containing 287,792,797 morphemes (token frequency), all of which were taken from
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88Japanese mental syllabaryeditions of the Asahi Newspaper printed from 1985 to 1998 (Amano & Kondo, 2000,
2003). Only basic 100 morae, excluding the 33 additional morae and the 3 special
sounds (see Appendix A of Tamaoka & Terao, 2004), were used to produce nonword
stimuli.
In light of high and low initial mora (CV1) frequencies, two types of CV1CV2CV3structured nonwords were created by a pair-matched selection. For example, aCV1CV2CV3-structured nonword ノケガ /no ke ga/ has a high initial mora frequency/no/, appearing 19,326,674 times in the newspaper lexical database of Amano andKondo (2000, 2003) calculated by Tamaoka and Makioka (2004). A CV1CV2CV3structured nonword with a low initial mora frequency ヌケガ /nu ke ga/ was pairedwith this nonword. The initial mora frequency of this nonword is 411,356 times.
The difference in initial mora frequencies between this pair was a quite large, being
18,915,318 times. Yet, /no ke ga/ and /nu ke ga/ share identical second CV2/ke/ andthird CV3/ga/ morae.
Furthermore, since it is known that initial phonemes affect the terminating pointof a voice-key device for measuring naming latencies (e.g., Sakuma, Fushimi, &Tatsumi, 1997; Tamaoka & Hatsuzuka, 1997; Yamada & Tamaoka, 2003), the same
initial consonant /n/ was selected for the pair. Thus, the only difference in the pair isactually the first vowel /o/ and /u/ included in CV1 . In addition, since vowel repetition
such as /no ko go/ is likely to slow down Japanese naming latency (Tamaoka & Murata,
2001; Tamaoka, Makioka, & Murata, 2004), three different vowels in CV1CV2CV3
were selected for producing nonword items. In this manner, 20 sets, or pairs (40nonwords), were created as shown in Appendix 1. A set of 40 CVCVCV-structured
dummy nonwords (not analyzed) were mixed with the aforementioned 40 experimental
nonwords, so that a total of 80 nonwords (40 experimental + 40 dummy nonwords)
were created for Experiment 1. All these nonwords were visually presented in the
katakana script, which is generally used to present foreign loanwords consisting ofa fewer proportion of Japanese lexical items.
The means and standard deviations of initial mora and bi-mora frequencies arereported in Table 3. Paired sample t-tests were conducted on frequencies of 20 sets ofstimuli. As expected, two nonword groups of high and low initial mora frequencies
significantly differed, t(19) = 6.085, p < .001. Three-mora CV1CV2CV3 -structured
stimuli were divided into two left- and right-side bi-mora CVCV combinations. A
Table 3
CV1CV2CV3-structured nonwords comparison of high- and low-frequency first mora
Condition of mora
frequency

First mora frequency

Left side

Right side

M

SD

M

SD

M

SD

High

9,633,236

6,822,595

2134

2945

2266

4486

Low

    652,803     492,691

2259

5045

2266

4486

Note 1: 20 stimulus items; M = mean; SD = standard deviation.
Note 2: “Left side” refers to CV 1 ,CV2 bi-mora frequency while “right side” to CV2CV3 frequency.

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K. Tamaoka, S. Makioka

89

paired sample t-test was conducted for bi-mora frequencies on the left side (bi-morafrequencies of CV1 and CV2) between nonwords of the high and low initial mora
(CV1) frequencies. Results showed no significant difference, t(19) = –0.214, p = .833.
Bi-mora frequencies on the right side (CV2 and CV3) were identical. As such, these 20
sets of nonwords were suitable for examining the effect of initial mora frequency.
4.1.3
Procedure

Stimulus items of CVCVCV-structured nonwords were randomly presented to participants in katakana at the center of a computer screen 600 ms after the appearanceof an eye fixation point marked by an asterisk (“*”). Participants were required topronounce each visually-presented nonword appearing on the screen as quickly andaccurately as possible. A voice-activated key turned off the timer to measure thenaming latency. The correctness of pronunciation was entered into the computer by
the examiner as being either “correct” or “incorrect.” The next fixation point waspresented 600 ms after the examiner pressed the key.
4.2
Analysis and results

Only correctly pronounced stimulus items were used in the analyses of naming
latencies. No extremes in naming latencies (less than 200 ms and longer than 2000ms) were found. Before performing the analysis, reaction times outside 2.5 standard
deviations at both the high and low ranges were replaced by boundaries indicated by
2.5 standard deviations from the individual means of participants in each category.
The statistical tests which follow analyze both participant (F1) and item (F 2) variability. The means and standard deviations of naming latencies and error rates of thehigh and low initial mora frequencies of CVCVCV-structured nonwords are shownin Table 4.
One-way ANOVAs with repeated measures were conducted for naming latencies.
The results indicated that nonwords with high initial mora frequency were named faster(i.e., exhibited shorter naming latencies) than those with low initial mora frequency inboth participant analysis, F1(1, 27) = 6.657, p < .05, and item analysis, F 2(1, 19) = 6.510,
p < .05. Unlike naming latencies, nonwords with high initial mora frequency resulted inerror rates equal to those with low initial mora frequency in both participant analysis,
F1(1, 27) = 0.006, p = .939, and item analysis, F 2(1, 19) = 0.023, p = .880.
Table 4
Naming latencies and error rates for three mora nonwords in Experiment 1
Naming latencies (ms)Error rates (%)

First-mora condition

M

SD

M

SD

High frequency
Low frequency

717
739

95
108

13.21
13.04

10.11
10.66

Note: n = 28 participants; M = mean; SD = standard deviation

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90Japanese mental syllabary

4.3
Discussion

In order to prove the effects of mora frequencies, Experiment 1 was conducted bycomparing CV1CV2CV3-structured nonwords with high and low initial mora frequencies. Results indicated that nonwords with a high initial mora frequency were named
faster than those with the low frequency initial mora. Experiment 1 controlled bi-morafrequencies of CV1 + CV2, and CV2 + CV3 using identical morae for both CV2 and CV3.
Since the stimulus items used in Experiment 1 were all nonwords, the mora frequency
effect influencing naming speed was independent from lexical involvement. At themoraic level, this finding thus supports the idea of the mental syllabary (Levelt et al.,1999) as being applicable to the core 100 moraic units. However, as discussed in Figure2, all Japanese morae can be classified by the concept of a light (e.g., CV,CSV) or aheavy (e.g., CVN, CVR) syllable. Experiment 2 further investigated the possibilityof the syllable as a stored unit of the mental syllabary.

Experiment 2: Comparison of CVNCV- and5 CVCVCV-structured
nonwords
In Experiment 2, CV1N2CV3-structured nonwords were comprised of three morae(CV1,N2, and CV3) or two syllables (CV1N2, and CV3). In contrast, there were three
CV1CV2,CV3-structured nonwords in either a mora or a syllable unit (CV1,CV2, andCV3). The primary assumption of Experiment 2 was that if a moraic unit is used fornaming (three mora units in both cases), both CV1N2CV3- and CV1CV2CV3-structured
nonwords should exhibit similar naming latencies. However, if syllabic units are used
for naming (two versus three syllabic units), CV1N2CV3-structured nonwords must
be named faster than CV1CV2CV3-structured nonwords. Thus, in order to examine
whether or not CVN is a speech production unit equal to CV, Experiment 2 wasconducted to compare CVNCV- and CVCVCV-structured nonwords with the samebi-mora frequencies in naming latencies and error rates.
5.1
Method
5.1.1
Participants

The same as Experiment 1.
5.1.2
Stimuli

CV1N1CV3 - and CV1CV1CV3 -structured nonwords were created by a pair-matchedapproach. The definition of nonwords and the lexical database (Amano & Kondo, 2000,
2003) used for stimulus selection were the same as in Experiment 1. Again, only basic
100 morae and the special sound /N/ were used to construct target nonword stimuli. For
example, the CV1N1CV3 -structured nonword マンブ /ma N bu/ has a bi-mora frequencyof 448,183 times for CV1N1 and 365,135 times for N1CV3 . Paired with this nonword,
another CV1CV1CV3 -structured nonword マデス /ma de su/ was selected by keeping theCV1 initial mora /ma/ the same. This nonword has a CV1 and CV2 bi-mora frequency
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Table 5
Stimuli of three mora nonwords with the same bi-mora frequencies in Experiment 2
Three mora conditions

CV1N2CV3
CV1CV2CV3

Left side (1st and 2nd morae)

Right side (2nd and 3rd morae)

M

SD

M

SD

153,172
152,759

126,850
136,066

121,388
119,382

85,79492,511

Note: 24 stimulus items; M = mean; SD = standard deviation.

of 497,101 times and a CV2 and CV3 bi-mora frequency of 277,152 times, both of which
are similar to the bi-mora frequencies of the nonword /ma N bu/. In this approach, 24
sets or pairs (48 nonwords) were created as shown in Appendix 2. An additional 48
CVCVCV-structured and 24 CVRCV-structured dummy nonwords (not analyzed inExperiment 2) were mixed with these 48 experimental nonwords. Among dummy items,
24 CVCVCV-structured and 24 CVRCV-structured nonwords were actually experimental
items for Experiment 3, so that target nonwords for both Experiments 2 and 3 can act
as dummy items for each other. In sum, a total of 120 nonwords (48 experimental items
for Experiment 2, 48 dummy items, also used as target items in Experiment 3, and 24
dummy items not used for either experiment) were created for Experiment 2.
The initial mora of CV1CV2CV3 -structured and CV1N2CV3 -structured nonwords
were the same in each pair, so that the effects of initial phonemes triggering a voicekey device (e.g., Sakuma, Fushimi, & Tatsumi, 1997; Tamaoka & Hatsuzuka, 1997;
Yamada & Tamaoka, 2003) were not expected to influence the measurement of
naming latencies. Again, as with Experiment 1, different vowels were selected foreach nonword item to avoid any same-vowel repetition effect (Tamaoka, Makioka,& Murata, 2004; Tamaoka & Murata, 2001).
The means and standard deviations of bi-mora frequencies are reported in Table 5.
Paired-sample t-tests were conducted on frequencies of 24 sets of stimuli. Three-mora
CV1N1CV3 - and CV1CV2CV3 -structured nonwords were divided into two left- andright-side bi-mora combinations. A paired-sample t-test was conducted for nonwordsof bi-mora frequencies separately on the left side (a combination of the first andsecond morae) and on the right side (a combination of the second and third morae).
Results showed no significant difference on either the left, t(23) = 0.038, p = .970, or
the right, t(23) = 0.166, p = .869, side. Thus, these 24 sets of nonwords were suitablefor examining CVN as being a speech production unit equal to CV.
5.1.3
Procedure

The same as Experiment 1.
5.2
Analysis and results

Only correctly pronounced stimulus items were used in the analyses of naming latencies. No extremes in naming latencies (less than 200 ms and longer than 2000 ms)
Language and Speech

92Japanese mental syllabary

Table 6
Naming latencies and error rates for three mora nonwords m Experiment 2
Naming latencies (ms)Error rates (%)

Three mora conditions

M

SD

M

SD

CV1N2CV3
CV1CV2CV3

654
735

71
100

1.49
7.44

2.33
5.71

Note: n = 28 participants; M = mean; SD = standard deviation.

were found. Before performing the analysis, reaction times outside of 2.5 standard
deviations at both the high and low ranges were replaced by boundaries indicated by
2.5 standard deviations from the individual means of participants in each category.
The statistical tests which follow analyze both participant (F1) and item (F1) variability. The means and standard deviations of naming latencies and error rates of
CVNCV- and CVCVCV-structured nonwords are shown in Table 6.
One-way ANOVAs with repeated measures were conducted for naming latencies.
The results indicated that CVNCV-structured nonwords were named 81 ms fasterthan CVCVCV-structured nonwords in both participant analysis, F1(1, 27) = 93.988,
p < .001, and item analysis, F 2(1, 23) = 85.768, p < .001. Likewise, CVNCV-structured
nonwords exhibited error rates which were 5.95% lower than those for CVCVCVstructured nonwords in both participant analysis, F1(1, 27) = 32.927, p < .001, anditem analysis, F 2(1, 23) = 7.894, p < .01.
5.3
Discussion

Experiment 2 was conducted to investigate whether or not a CVN heavy syllable
is a speech production unit as well as a CV light syllable (equal to a CV mora).
Controlling bi-mora frequencies with the same initial mora, Experiment 2 revealed
that CVNCV-structured nonwords had shorter naming latencies and lower error
rates than CVCVCV-structured nonwords. Since bi-mora frequencies between CVN
and CVCV were the same, CVN heavy syllables must contribute to ready-mademotor-programs stored in the Japanese mental syllabary as variations of the 100
core morae or light syllables. Thus, the findings of Experiment 2 support the viewof a syllable unit as implicated in Japanese speech production. Experiment 3 further
investigated the heavy syllable CVR as another potential candidate involved in theJapanese metal syllabary.

Experiment 3: Comparison of CVRCV- and6 CVCVCV-structured
nonwords
The same assumption for the CVN unit in Experiment 2 was extended to the CVR
unit in Experiment 3. To investigate CVR as being a speech production unit equal
to CV, Experiment 3 was conducted to compare CVRCV- and CVCVCV-structured
nonwords with the same bi-mora frequencies in naming latencies and error rates.
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6.1
Method
6.1.1
Participants

The same as Experiments 1 and 2.
6.1.2
StimuliAs with Experiment 2, CV1R2CV3- and CV1CV2CV3-structured nonwords were createdby a pair-matched approach. Again, only 100 basic morae and the special sound /R/
were used to construct nonword target stimuli. For example, the CV1R2CV3-structured
nonword キーゼ /ki R ze/ has 113,212 times of bi-mora frequency for CV1R2 and 87,867
times for R2CV3. Paired with this nonword, a CV1CV2CV3-structured nonword キロ
ガ /ki ro ga/ was selected due to its sharing the CV1 initial mora, /ki/. This nonword
has a CV1 and CV2 bi-mora frequency of 123,726 times and a CV2 and CV3 bi-morafrequency of 83,285 times. This pair shares similar bi-mora frequencies. In thisapproach, 24 sets or pairs (48 nonwords) were created as shown in Appendix 3. In addition, different 48 CVCVCV-structured and 24 CVNCV-structured dummy nonwords(not analyzed) were mixed with these 40 experimental nonwords. As explained inExperiment 2, 24 CVNCV-structured and 24 CVCVCV-structured dummy nonwords
were used for experimental items in Experiment 2. A total of 120 nonwords (48 targets
for Experiment 3, 48 dummy items also used in Experiment 2, and 24 items not used
for either experiment) were used for Experiment 3. The initial morae of CV1CV2CV3structured and CV1R2CV3-structured nonwords were identical in each pair. As with
Experiments 1 and 2, different vowels were selected for each nonword.
The means and standard deviations of bi-mora frequencies are reported inTable 7. Paired-sample t-tests were conducted on the frequencies of 24 sets ofstimuli. Three-mora CV1R 2 CV3 - and CV1CV2 CV3 -structured nonwords weredivided into two left- and right-side bi-mora combinations. A paired sample t-test
was conducted for nonwords of bi-mora frequencies separately on the left side
and on the right side. Results showed no significant difference on either the left,t(23) = 0.149, p = .883, or right, t(23) = 0.721, p = .478, side. Thus, these 24 sets of
nonwords were suitable for examining CVR as being a speech production unit
equal to CV.

Table 7
Stimuli of three mora nonwords with the same bi-mora frequencies in Experiment 3
Left side (1st and 2nd morae)
Three mora conditions
CV1R2CV3
CV1CV2CV3

Right side (2nd and 3rd morae)

M

SD

M

SD

14,574
14,46728,802
29,173

14,267
13,53927,131
24,398

Note: 24 stimulus items; M = mean; SD = standard deviation.

Language and Speech

94Japanese mental syllabary

6.1.3
Procedure

The same as Experiments 1 and 2.
6.2
Analysis and results

Only correctly pronounced stimulus items were used in the analyses of naming latencies. No extremes in naming latencies (less than 200 ms and longer than 2000 ms)
were found. Before performing the analysis, reaction times outside of 2.5 standard
deviations at both the high and low ranges were replaced by boundaries indicated by
2.5 standard deviations from the individual means of participants in each category.
The statistical tests which follow analyze both participant (F1) and item (F 2) variability. The means and standard deviations of naming latencies and error rates of
CVRCV- and CVCVCV-structured nonwords are shown in Table 8.
Table 8
Naming latencies and error rates for three mora nonwords in Experiment 3
Naming latencies (ms)Error rates (%)

Three mora conditions

M

SD

M

SD

CV1R2CV3
CV1CV2CV3

643
737

66
102

4.17
11.46

4.24
9.12

Note: n = 28 participants; M = mean; SD = standard deviation.

One-way ANOVAs with repeated measures were conducted for naming
latencies. The results indicated that CVRCV-structured nonwords were named
94 ms faster than CVCVCV-structured nonwords in both participant analysis,
F1(1, 27) = 102.782, p < .001, and item analysis, F 2(1, 23) = 99.432, p < .001. Likewise,
CVRCV-structured nonwords showed 7.29% fewer errors than CVCVCV-structured
nonwords in both participant analysis, F1(1, 27) = 25.941, p < .001, and item analysis,
F 2(1, 23) = 9.304, p < .01.
6.3
Discussion

Experiment 3 was conducted to investigate whether or not a CVR heavy syllable is a
speech production unit as well as a CV light syllable. Controlling bi-mora frequencies
with the same initial mora, Experiment 3 revealed that CVRCV-structured nonwords
have shorter naming latencies and lower error rates than CVCVCV-structured
nonwords. As with Experiment 2, bi-mora frequencies between CVR and CVCV
were the same. Thus, units of CVR heavy syllables may also contribute to ready-mademotor-programs stored in the Japanese mental syllabary. Consequently, as shown in
Figure 2, both the CVN (Experiment 2) and CVR (Experiment 3) units are processed
for Japanese speech production as syllabic units prepared in the mental syllabary as
variations of the 100 core morae or light syllables.
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95

Additional analysis of Experiments 2 and 3 and
7 further
discussion
Based on the findings of Experiments 2 and 3, both heavy syllabic units of CVN and
CVR were assumed to reflect two variations of the 100 core morae. Thus, similarities innaming latencies and error rates were further tested by directly comparing the 24 sets of
CVNCV- and CVRCV-structured nonwords. Naming latencies and error rates of CVNCV
nonwords in Experiment 2 and the CVRCV nonwords in Experiment 3 were comparedby a t-test of independent samples. The results showed no significant difference in naming
latencies, t(46) = 0.190, p = .850, but a significant difference in error rates, t(46) = 2.932, p < .01.
The CVN units were phonologically processed with equal naming latencies as the CVR
units, although the CVN units were more easily named than the CVR units. In addition,
the 24 sets of CVCVCV-structured nonwords used as counter stimuli for Experiments 2
and 3 showed no significant difference in both naming latencies, t(46) = 1.033, p = .307,
and error rates, t(46) = 1.464, p = .150. Since naming latencies are considered to be moresensitive as a measurement index than error rates, it would be safe to state that heavy
syllables comprised of CVN (Experiment 2) and CVR (Experiment 3) units may contributeto ready-made motor-programs stored in the Japanese mental syllabary. Consequently, as
shown in Figure 3, both the CVN and CVR units are likely to be processed for Japanese
speech production as syllable units prepared in the mental syllabary as variations of the
100 core morae or light syllables. However, it should be noted that this direct comparisonof Experiments 2 and 3 provides only partial evidence for the existence of CVN and CVR
units in the mental syllabary, since initial consonants and bi-mora frequencies of the two
sets of nonwords did not match across both experiments.

8 Experiment 4: Effects of bi-mora frequency

In order to investigate the effects of bi-mora frequency, Experiment 4 also used anaming task which required participants to pronounce three-mora CVCVCV-structured
nonwords with high bi-mora frequencies (e.g., /ki no be/) and low bi-mora frequencies(e.g., /ki be no/) using identical moraic units with different bi-mora combinations.
8.1
Method8.1.1
Participants

Twenty-eight graduate and undergraduate students (22 females and 6 males) at
Hiroshima University in Japan, all native speakers of Japanese, participated inExperiment 4. None of these participants took part in Experiments 1–3. Ages ranged
from 21 years and 1 month to 29 years and 0 months, with the average age being 23
years and 2 months on the day of testing.
8.1.2
StimuliTwo types of CV1CV2CV3-structured nonwords were created by recombining the same
three morae: Nonwords with high bi-mora frequency (e.g., CV1CV2CV3, キノベ, /ki no
Language and Speech

96Japanese mental syllabarybe/) and nonwords with low bi-mora frequency (e.g., CV1CV3CV2,キベノ, /ki be no/).
In this method of stimulus selection, since the morae remained unchanged in the two
conditions, mora frequencies of each paired-stimuli (e.g., /ki no be/ versus /ki be no/)were identical. A total of 48 nonwords (24 sets) were presented in katakana (see detailsof stimulus items in Appendix 4). Again, only the 100 basic morae were used to produce
target nonword stimuli.
Bi-mora frequencies are reported in Table 9. Paired sample t-tests were conducted
on 24 sets of stimuli. Three-mora CVCVCV-structured stimuli were divided into twoleft- and right-side bi-mora CVCV combinations. A paired sample t-test was conducted
for bi-mora frequencies on the left side between the conditions of high and low bi-morafrequencies. Results indicated a significant difference between the two conditions,t(23) = 4.097, p < .001. The same analysis was carried out for the right side, which
also indicated a significant difference, t(23) = 6.157, p < .001. Furthermore, bi-morafrequencies of the left and right sides together were examined by the same t-test.
The result showed a significant difference between the two conditions, t(23) = 7.137,
p < .001. The same t-tests were conducted on bi-mora frequencies between the left andright sides within the same high or low bi-mora frequencies. The results showed nosignificant differences in either of the conditions of high bi-mora frequency, t(23) =0.528, p = .603, or low bi-mora frequency, t(23) = –0.147, p = .884. The results of theset-tests suggest that the category of high and low bi-mora conditions was appropriately
divided to investigate the effect of bi-mora frequency. In addition, as presented inAppendix 4, the initial morae of all nonwords were controlled to be the same. The
effect of the initial phoneme observed in the naming task measuring naming latencies(e.g., Sakuma et al., 1997; Tamaoka & Hatsuzuka, 1997; Yamada & Tamaoka, 2003)
was therefore avoided.
Table 9Bi-mora frequencies of CVCVCV-structured three-mora nonwords used in Experiment 4
Left side

Right side

Both together

Bi-mora condition

M

SD

M

SD

M

SD

High frequency

141,761

167,337

119,617

93,058261,378

176,162

Low frequency

2,329

1,655

2,403

1,448

4,731

1,911

Note: M = mean; SD = standard deviation.
8.1.3
Procedure

The same as for Experiments 1–3.
8.2
Analysis and results

Only correctly pronounced stimulus items were used in the analyses of naming latencies. No extremes in naming latencies (less than 200 ms and longer than 2000 ms)
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97

were found. Before performing the analysis, reaction times outside of 2.5 standard
deviations at both the high and low ranges were replaced by boundaries indicated by
2.5 standard deviations from the individual means of participants in each category.
The statistical tests which follow analyze both subject (F1) and item (F 2) variability.
The means and standard deviations of naming latencies of two nonword conditions
are shown in Table 10.
Table 10
Naming latencies and error rates for three-mora nonwords in Experiment 4
Naming latencies (ms)Error rates (%)

Bi-mora condition

M

SD

M

SD

High frequency
Low frequency

573
607

89
93

4.69
8.85

5.26
9.30

Note: n = 28 participants; M = mean; SD = standard deviation.

One-way ANOVAs with repeated measures indicated that nonwords with high bi-morafrequencies exhibited shorter naming latencies than those with low bi-mora frequencies
in participant analysis, F1(1, 27) = 27.83, p < .001, but not in item analysis, F 2(1, 23) =2.64, p = .118. Likewise, nonwords with high bi-mora frequency resulted in significantly
lower error rates than those with low bi-mora frequencies in both participant, F1(1, 27)= 7.67, p < .05, and item analysis, F 2(1, 23) = 8.90, p < .01. Thus, bi-mora frequency
seems to be an important factor influencing speed and accuracy in the phonologicalprocessing of nonwords. However, as indicated by the null effect of naming latencies
in the item analysis, some mora combinations seem to behave unpredictably.
8.3
Discussion

Experiment 4 showed significant effects of bi-mora frequency, while the item analysis
on naming latency did not show any significance. Generally speaking, with some
irregularity in bi-mora combinations, Experiment 4 demonstrated that the threemora CVCVCV-structured nonwords with high bi-mora frequency were named more
quickly and accurately than those with low bi-mora frequency. The frequency featureof two-mora combinations appears to affect Japanese phonological processing.
However, it is quite possible that bi-mora frequencies might only be apparent in theprocessing of nonwords. Thus, Experiment 5 investigated bi-mora frequency together
with word frequency.

Experiment 5: Effects of word and bi-mora
9 frequencies
Experiment 5 conducted a naming task requiring participants to pronounce threemora CVCVCV-structured real words with high word and high bi-mora frequencies(e.g., /ka ta ti/), with high word and low bi-mora frequencies (e.g., /ka ta ki/), and withlow word and low bi-mora frequencies (e.g., /ka yu mi/).
Language and Speech

98Japanese mental syllabary

9.1
Method
9.1.1
Participants

Twenty-five graduate and undergraduate students (10 females and 15 males) at
Hiroshima University in Japan, all native speakers of Japanese, participated inExperiment 5. None of these participants were involved in Experiments 1–4. Ages
ranged from 21 years and 7 months to 30 years and 9 months, with the average age
being 25 years and 3 months on the day of testing.
9.1.2
Stimuli

Experiment 5 used three sets of 24 CVCVCV-structured real words with (1) high bi-mora
and high word frequency, (2) high bi-mora frequency and low word frequency, and (3) low
bi-mora frequency and low word frequency (a total of 72 real words). Combinations of highword frequency with low bi-mora frequency do not exist. Initial consonants were controlled
to be the same throughout each pair of the three conditions (see details of stimulus items
in Appendix 5). Only the 100 basic morae were used for constructing nonword stimuli.
The means of mora, bi-mora, and word frequencies are reported in Table 11.
One-way ANOVAs were conducted on these frequencies. Mora frequency showedno significant main effect. The bi-mora frequency showed a significant main effect,
F(2, 69) = 35.739, p < .001. Scheffe’s multiple comparisons indicated that the conditionof low word and low bi-mora frequencies was significantly lower in bi-mora frequency
than the other two conditions. Likewise, word frequency showed a significant main
effect, F(2, 69) = 75.615, p < .001. Scheffe’s multiple comparisons indicated that thecondition of high word and high bi-mora frequency was significantly higher in word
frequency than the other two conditions. Thus, real words stimuli in Experiment 5
were suitable for examining bi-mora and word frequency effects independent frommora frequency, and furthermore the three real word conditions could allow theindependent observation of bi-mora and word frequencies.

Table 11
Word and bi-mora frequencies for three-mora CVCVCV-structured real words used inExperiment 5
Mora frequency

Word frequency

		 M

		 M

Condition of frequencies

		 M

High word and high bi-morafrequencies
Low word and high bi-morafrequencies
Low word and low bi-morafrequencies

1,418,147 393,312 407,598

213,688 27,875

1,340,683 521,743 409,965

245,403

274

117

13,694	    2,721

264

220

906,392 328,596

Note: M = mean; SD = standard deviation.

Language and SpeechSDBi-mora frequency
SDSD
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9.1.3
Procedure

The same as for Experiments 1–4.
9.2
Analysis and results

Only correctly pronounced stimulus items were used in the analyses of naming latencies. No extremes in naming latencies (less than 200 ms and longer than 2000 ms) were
found. Before performing the analysis, reaction times outside of 2.5 standard deviations
at both the high and low ranges were replaced by boundaries indicated by 2.5 standard
deviations from the individual means of participants in each category. The statistical tests
which follow analyze both subject (F1) and item (F2) variability. The means and standard
deviations of naming latencies of three real words conditions are shown in Table 12.
Table 12
Naming latencies and error rates for three-mora CVCVCV-structured real words inExperiment 5
Stimulus type Condition of frequencies

Naming latencies (ms)

		

M

SD

M

SD

524

71

2.33

2.97

539

72

3.00

5.54

548

72

1.83

3.42

Real words

High word and highbi-mora frequencies
Low word andhigh bi-mora frequencies
Low word and low
bi-mora frequencies

Error rates (%)

Note: n = 25 participants; M = mean; SD = standard deviation.

One-way ANOVAs with repeated measures indicated main effects on naming
latencies in participant analysis, F1(2, 48) = 31.94, p < .001, and in item analysis,
F 2 (2, 46) = 3.449, p < .05. Further analyses showed that real words with high word
frequency and high bi-mora frequency had significantly shorter naming latencies
than real words with low word frequency and high bi-mora frequency in participant
analysis, F1(1, 24) = 35.47, p < .001, but a significant tendency in item analysis, F 2(1,23) = 3.44, p = .077. Again, real words with high word frequency and high bi-morafrequency and real words with low word frequency showed significantly shorter
latencies than low bi-mora frequency in both participant and item analyses, F1(1, 24)= 62.06, p < .001; F 2(1, 23) = 5.15, p < .05, which can be considered to be the effectof word frequency. The naming latencies of real words with low word frequency andhigh bi-mora frequency were shorter than those of words with low word frequency
and low bi-mora frequency in participant analysis, F1(1, 24) = 6.99, p < .05, but not
in item analysis, F 2(1, 23) = 0.66, p = .425. Since word frequencies were low in bothconditions, participant analysis supported an effect of bi-mora frequency, but item
analysis did not support this result. Unlike naming latencies, error rates of real words
did not show any main effect. This result indicates that neither word nor bi-morafrequency has an effect upon error rates of real word naming.
Language and Speech100Japanese mental syllabary

9.3
Discussion

The results of Experiment 5 were subtle. The facilitation effect of word frequency was
supported by comparisons of three real word conditions. Naming of real words withhigh word frequency was faster than the other real word conditions with low word
frequency. However, the effect of bi-mora frequency was not fully supported by thecomparisons. Under the conditions of low word frequency, naming of real words withhigh bi-mora frequency was significantly faster than those with low bi-mora frequency,
as was indicated by participant analysis but not item analysis. To some degree, similar
to null significance in item analysis on naming latencies in Experiment 4 (but significant
in both participant and item analyses on error rates), bi-mora frequencies in Experiment
5 had only a minor effect on the phonological processing of real words.

10 General discussion

The present study tested how each unit of mora, syllable, bi-mora and word affects
speech production of Japanese using a naming task of visually-presented nonwords
and real words.
10.1
Effects of initial more frequency—the 100 core moraic units

In order to examine whether or not the mental syllabary as proposed by Levelt et al.
(1999) exists in the Japanese language, Experiment 1 was conducted to compare naming
speed of CVCVCV-structured nonwords with high and low initial mora frequencies.
Results indicated that nonwords with a high initial mora frequency were named
faster than those with low frequency initial mora. Stimulus items in Experiment 1
were all nonwords with controlled bi-mora frequencies of CV1+ CV2, and CV2 + CV3
using identical morae for both CV2 and CV3. The effects of mora frequency wereseen to influence the speed of production independent of lexical involvement. It ishypothesized that if stored representations corresponding to moraic units are retrieved
from the Japanese mental syllabary, the retrieval process should be sensitive to theirfrequencies. Therefore, the finding of frequency effects in Experiment 1 supported
the concept of mental syllabary at the Japanese moraic level. As soon as Japanesenative speakers see nonwords of a CVCVCV-structured three-mora string presented
in katakana moraic symbols, they retrieve the corresponding mora articulatory units
from the mental syllabary. Experiment 1 suggested that Japanese morae seem to play
an important role for the interface between phonological and phonetic encoding.
Applying the idea of the mental syllabary by Levelt et al. (1999), it is proposed that alimited number of frequently-used phonological moraic units are stored as ready-mademotor-programs in the Japanese mental syllabary. Since Japanese sounds consist of
only the 100 core moraic units, all of them will be candidates. In fact, effects of syllable
frequencies in Experiment 1 were found within the 100 core morae. In addition to the
100 morae and the three special sounds (i.e., /N/, /R/, and /Q/), a list of an additional 33
morae was officially announced by the Government of Japan (1991), but these 33 morae
are exclusively used for alphabetic loanwords whose sounds cannot be described within
traditional Japanese moraic units. Therefore, as depicted in Figure 3, the present study
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101

assumes that only the 100 core moraic units must be stored in the mental syllabaryas pre-compiled motor programs ready made for speech production. However, once
these 33 additional morae come to be used frequently in the Japanese language, they
may possibly come to be stored in the Japanese mental syllabary in the future.
Levelt and his colleagues did not specify actual syllabic units stored in the mental
syllabary in Dutch, English, or German. Since the Japanese language requires only
a limited number of syllabic or moraic units for speech production, the present study
proposed the 100 core moraic units to be the actual units stored in the Japanese mental
syllabary. In this sense, the Japanese language is an ideal example to investigate themental syllabary. Yet, a further possibility remains of the syllable, rather than mora(see Figure 1) as a unit of Japanese speech production.
Figure 3
Structure of the Japanese mental syllabary for speech production

Note 1: “CV” in this figure represents all light syllables including ØV, CV, ØSV and O.
Note 2: A thicker line indicates faster activation of a real word in the Phonological lexicon.
Note 3: A dotted line indicates weaker inf luence to activate a real word in the phonologicallexicon.

Language and Speech

102Japanese mental syllabary

10.2
Light and heavy syllables in the Japanese mental syllabary

At the stage of Experiment 1, the repository of the mental syllabary was assumed to
be stored as units of moraic size. From the perspective of syllabic units, all 100 morae
can be combined with /N/ (nasal) and /R/ (long vowel) to produce heavy syllables.
As discussed in Figure 1, in that all Japanese morae can be classified by the conceptof a light (e.g., ØV, CV, ØSV, CSV) or a heavy (e.g., ØVN, CVN, ØSVN, CSVN,
ØVR, CVR, ØSVR, and CSVR) syllable, the syllable is seen to serve as the unit ofJapanese speech production. Thus, Experiments 2 and 3 were conducted to clarify
the possibility of light/heavy syllables as being stored as representations within theJapanese mental syllabary.
Experiment 2 indicated that CVNCV-structured nonwords had shorter naming
latencies and lower error rates than CVCVCV-structured nonwords. Similar to theinvestigation of CVN heavy syllables in Experiment 2, Experiment 3 considered
the possibility of CVR heavy syllables as speech production units. The results ofExperiment 3 also revealed that CVRCV-structured nonwords have shorter naming
latencies and yield lower error rates than CVCVCV-structured nonwords. Results ofExperiments 2 and 3 confirmed the previous findings of Tamaoka and Terao (2004)
under more scrupulous stimulus conditions by controlling bi-mora frequencies of
nonwords. Since bi-mora frequencies between CVN/CVR and CVCV were the same
in both Experiments 2 and 3, this consistent finding further illustrated the involvement of heavy syllables in the mental syllabary. Stored units in the mental syllabary
may be regarded as both the core 100 light syllables (rather than morae) and their two
variations of +N/+R heavy syllables as depicted in Figure 3.
The results of Experiments 1–3 furthermore suggest that heavy syllables comprisedof CVN and CVR units contribute to ready-made motor-programs stored in theJapanese mental syllabary as variations of 100 light syllables. This supports previous
findings regarding the importance of the syllable as a relevant unit in speech production in various languages such as Chinese (Chen, Lin, & Ferrand, 2003), Dutch(Cholin et al., 2006; Cholin, Schiller, & Levelt, 2004; Schiller, 1998, 1999; Schiller
et al., 1996), English (Ferrand, Segui, & Humphreys, 1997; Schiller, 1999; 2000) andSpanish (Carreiras & Perea, 2004). In this sense, the syllable is a much more universal
unit for speech production.
10.3
Effects of bi-mora and word frequency

Experiment 4 tested the assumption that high-frequency bi-morae would be activatedmore quickly and accurately than less frequently-used bi-morae, independent of
mora and word frequencies. In accordance with this assumption, the overall resultof Experiment 4 was that the three-mora CVCVCV-structured nonwords with highbi-mora frequency were named more quickly and accurately than those with low
bi-mora frequency. However, it should be noted that the item analysis of naming
latency showed no significant effect, p = .118, indicating that some bi-mora items
may affect speech production independent of bi-mora frequency. Bi-mora frequencies might only be apparent, to some degree, in the processing of nonwords; in otherwords, the possibility that bi-mora frequency influences phonological processing only
Language and Speech

K. Tamaoka, S. Makioka

103

in the absence of lexical status cannot be totally disregarded. Therefore, Experiment
5 investigated bi-mora frequency together with word frequency.
As had been anticipated in previous studies on the effects of word frequency (e.g.,
Fushimi et al., 1999; Hino & Lupker, 1998; Tamaoka & Takahashi, 1999), Experiment
5 demonstrated the facilitation effects of word frequency. Results indicated that thenaming of real words with high word frequency was quicker than other real word conditions with low word frequency. Experiment 5 focused on the effect of bi-mora frequency
when co-existing with word frequency. Naming latencies of real words with high bi-morafrequencies were significantly shorter than those with low bi-mora frequencies, as wasindicated by participant analysis and a significant tendency in item analysis, p = .077.
Unlike the clear trend in error rates between high and low bi-mora frequencies shown inExperiment 4, error rates in Experiment 5 did not reveal any difference between words
with high and low bi-mora frequencies. Although comparison of high and low bi-morafrequencies within low word frequency showed a minor effect on naming latencies forreal words, the effect of bi-mora frequency was not as clear-cut as had been expected.
A more general understanding of the three CVCVCV-structured real words canbe achieved through the activation process, as follows. During the processing ofreal words, the ready-made motor-programs of light/heavy syllables (see Figure 3)
are retrieved from the mental syllabary for speech production. For example, the real
word “novel,” pronounced /syoR se tu/, is pronounced by three syllables /syoR/ (heavy
syllable CSVR), /se/ (light syllable CV) and /tu/ (light syllable CV), not by four morae/syo/, /R/, /se/ and /tu/. Since the initial light syllable (or mora) frequency affects the
speed of naming, vocalization takes place before activating a whole phonologicalrepresentation of the target word; at this stage, the bi-morae frequencies also come intoplay. However, since word frequencies have much stronger effects on the activation ofreal word phonological representations, bi-mora frequencies have only a minor effect.
The phonological representation of the whole word /syoRsetu/ could be reached byassembling three syllabic units of /syoR/, /se/ and /tu/, and by completing pronunciation
at the whole-word level prior to the apparent effects of bi-mora frequencies.
In contrast, since nonwords lack lexical status in the phonological lexicon, theirconstructing units are simply assembled at the syllabic level. In the process of assembly,
the effect of bi-mora frequencies becomes apparent since there is no influence of word
frequency. However, some bi-mora items showed irregular behavior (item analysis was
not significant in Experiment 4). Consequently, the present study found that bi-morafrequency has only a partial effect on the phonological processing of Japanese whenword frequency is apparent (i.e., real words); in contrast, when word frequency does
not exist (i.e., nonwords), bi-mora frequency has great bearing on speech production,
accepting the existence of some irregularly-behaved bi-mora combinations.
10.4
A methodological issue of the present study—naming task

The present study utilized a naming task which required participants to pronounce
a visually-presented katakana stimulus. This process can be regarded as “reading
aloud,” not natural speech production. However, discussion in the present study islimited to the function of the mental syllabary as the interface between phonological
Language and Speech

104Japanese mental syllabaryand phonetic encoding. Therefore, we assume that the same mechanism is shared
between reading aloud and natural speech production at the level of interface.
As visual presentations for the naming task in the present study, the katakana
script is used for the following two reasons: (1) the long vowel is represented by ahorizontal bar (hiragana cannot precisely represent long vowels), and (2) there are far
fewer lexical items presented in katakana (basically, only alphabetic loanwords). The
naming latencies of the present experiments include times required for katakana-tomora conversion. The inclusion of conversion time is necessary in implementing thenaming task. However, if the katakana-to-mora conversion is performed by one-to-one
mapping, the pronunciation of nonwords of CVCVCV-structured three light syllables(or three morae) presented by three katakana symbols should be processed at the same
speed as CVNCV-structured nonwords (also presented by three katakana symbols).
Interestingly, Experiments 2 and 3 showed the consistent result that CVNCV and
CVRCV nonwords including heavy syllables of CVN and CVR were named fasterthan their baseline of CVCVCV nonwords, even though all these items are represented
by the same number of three katakana symbols. In other words, CVN or CVR units
represented by two katakana symbols are possibly processed as a single syllabic
unit, equivalent to CV units represented by a single katakana symbol. Therefore,
mappings between katakana symbols and moraic sounds do not necessarily imply
exact conversion of one-to-one kana-to-mora correspondence for speech production.
In this sense, although naming latencies in Experiments 1–5 are bound to include
times required for katakana-to-mora conversion, they nevertheless seem to provide
meaningful indications for speech production.
To ascertain the function of the mental syllabary, Laganaro and Alario (2006)
utilized two experimental methods with a standard naming task: delayed naming andarticulatory suppression. The delayed naming task requires participants to pronounce
the visually-presented items after a short delay upon presentation of a response cue.
Laganaro and Alario found no effects of syllabic frequency in the delayed naming task,
although effects were apparent in the standard naming task. In addition, they also tested
the effects of syllabic frequencies by delayed naming with articulatory suppression. As
their method of articulatory suppression, the syllable /ba/ (“ba-ba- …” was presented
on the screen as well) required participants to repeat until the time the response cue
appeared on the screen. This time, syllabic frequency effects were observed. Laganaro
and Alario (2006) concluded that syllabic-sized representations were retrieved fromthe mental syllabary in the process of phonetic encoding. These two methods can beapplied to further studies investigating the Japanese mental syllabary.
10.5
Remaining questions for further studies

The present study reported five experiments. These experiments per se cannot lead toconcrete generalizations as to the mechanism of speech production in the Japanese
language. At the end of the present study, at least three unsolved questions remain.
First, the mental syllabary in the present study included CVN and CVR units ofheavy syllables. These heavy syllables are created by combining one of the 100 core
light syllables and the special moraic units of /N/ or /R/. This inclusion was supported
Language and Speech

K. Tamaoka, S. Makioka

105

by the consistent finding that CVNCV (Experiment 2) and CVRCV (Experiment 3)
units were processed for the naming task faster than their counter stimuli of CVCVCV(bi-mora frequencies being controlled in both conditions). As illustrated in Figure 3,
the present study assumed that heavy syllables are stored as a paired set of the 100
core light syllables (e.g., a set of /ka/, /kaN/ and /kaR/). However, it is still uncertain
whether these heavy syllables are represented in the mental syllabary or whether they
are actually created by phonological manipulation of /N/ or /R/ addition.
Second, the present study tested the special sounds of /N/ in Experiment 2 and /R/
in Experiment 3, which create a whole set of heavy syllables based on the 100 core
light syllables. The one remaining special sound /Q/ is untested because this moraic
sound only appears in middle of words as a voiceless obstruent placed before thefour consonants /p/, /t/, /k/ and /s/ such as /gaQki/ “musical instrument,” /kiQte/ “astamp.” As the present study proposed both light and heavy syllables as stored units
in the mental syllabary, a set of heavy syllables with /Q/ (i.e., CVQ) should also be
investigated as candidates of stored units in the Japanese mental syllabary.
Third, frequencies of mora (or light syllables) and bi-mora (including heavy syllables) in the present study were taken from the frequency database of Tamaoka andMakioka (2004). There are two possible biases inherent in this index. The databaseprovided by Tamaoka and Makioka was calculated on the basis of a written form ofa particular Japanese newspaper called the Asahi Newspaper. As for studies of speech
production, it is ideal to have mora and bi-mora frequencies calculated in the useof Japanese spoken corpus. Another bias could result from the fact that mora andbi-mora frequencies of Tamaoka and Makioka were calculated within lexical units.
However, actual speech production continues until the next pause separating each
unit. Ideally speaking, mora and bi-mora frequencies should be counted cross-lexically(i.e., including cross-word boundaries) as a unit divided by the point of each pause.

11 Summary

The present study puts forward the following five proposals in Japanese speech
production. First, frequency effects of mora (or light syllable) units among the 100
core morae were observed. Based on this finding, the present study proposes the 100
core light syllables (or morae) as fundamental ready-made motor-programs stored
in the mental syllabary used for speech production. Second, CVN and CVR units
displayed shorter naming latencies than their counter CVCV units. Thus, the present
study proposes these heavy syllables of CVN and CVR are also stored candidates in
the mental syllabary. Third, combining these two findings, the present study further
proposes that the 100 core light syllables and their two variations of CVN and CVR
heavy syllables are stored as 100 sets in the mental syllabary. In other words, thenumber of stored units is the 100 core light syllables and their two variations of heavy
syllables, totaling 300 syllabic units in all. Fourth, bi-mora frequencies showed someeffects on nonword naming, but only a minor effect on real word naming. This finding
proposes that bi-mora frequencies have some effects only on a string of non-lexical
items for speech production. Fifth, word frequency had substantial effects on real
word naming. Thus, the present study proposes that word frequencies are likely toovertake bi-mora frequencies, and to influence speech production of lexical items.
Language and Speech

106Japanese mental syllabary

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108Japanese mental syllabary

Appendix 1
The CV1CV2CV3-structured nonwords with high and low intitial mora (CVT) frequencies used for Experiment 1
Condition of high-frequency initial mora

Condition of low-frequency initial mora#   Nonword

Phoneme#   Nonword

Phoneme

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

he ro syu
ha pu kyoha mi boha ki zoga he toga mo bega se toga ko zeno re tino wa mino ke gano sya hi
tyo me yu
tyo bi hetyo ge bi
tyo zi nyu
zyo re gi
zyo ke nu
zyo hi mu
zyo hu pi

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

ha ro syu
he pu kyohe mi bohe ki zogu he togu mo begu se togu ko zenu re tinu wa minu ke ganu sya hi
tya me yu
tya bi hetya ge bi
tya zi nyu
zya re gi
zya ke nu
zya hi mu
zya hu pi

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K. Tamaoka, S. Makioka

109

Appendix 2
Three mora CVNCV- and CVCVCV-structured nonwords used for Experiment 2
CVNCV-structured nonwords

C VCVCV-structured nonwords#   Nonword

Phoneme#   Nonword

Phoneme1

ku N zo1

ku to re2

su N he2

su ro ba3

so N pa

3

so ku te4

ti N ha4

ti ko na5

to N ra5

to ha ti6

na N bi6

na su to7

no N zu7

no se gu8

hi N ze8

hi tu ka9hu N mi9

hu ta si10ma N bu

10

ma de su11

yo N be11

yo bu ti12

ra N bi

12

ra si se13

ro N bi13

ro ku na14

g i N se14

gi ka zu15

go N mu15

go ya bu16

za N mo16

za su te17

zo N ku17

zo re ka18

de N su18

de mo ku19

do N hi19

do ri ma20

bi N hu20

bi to ma21

be N yu21

be ki na22

bo N yu22

bo si ze23

pa N bo23

pa tu me24

pe N ro24

pe ki goLanguage and Speech110Japanese mental syllabary

Appendix 3
Three mora CVRCV-and CVCVCV-structured nonwords used for Experiment 3
CVRCV- structured nonwords

CVCVCV-structured nonwords#   Nonword

Phoneme#   Nonword

Phoneme

1
2
3
4
5
6
7
8

ka R soki R zekeR n isa R kose R p ota R pina R bunu R pi

1
2
3
4
5
6
7
8

ka hi meki ro ga
ke ba nu
sa do nise hi muta go bina ki bunu re ti9

ne R ma9

ne zi ga10

ha R be10

ha so mu11

hi R pa

11

hi ne gu12

hu R re12

hu ka ne13

he R bu13

he ko nu14

ma R bi

14

ma hi do15

mi R pu15

mi no ta16

me R yu16

me bi zu17

ya R pu

17

ya ni mo18

ra R mo18

ra do bu

19

ri R nu

19

ri hu sa20

re R hi

20

re zi ho21

za R bu

21

za be ko22

zi R yu22

za sa me23

da R he23

za hi ne24

bu R yo24

bu he zi

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Appendix 4
Nonwords with high and low bi-mora frequencies used for Experiment 4
High frequency bi-mora combinations

Low frequency bi-mora combinations#   Nonword

Phoneme#   Nonword

Phoneme1
2

ki no besa ki bo1
2

ki be nosa bo ki

3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

sa to zusa hi rosu me zata su gita no deta me guna zi yuni ga reni to meni ho keha ti rohi ku sehu to ba
be ki moho ku gi

3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

sa zu tosa ro hisu za meta gi suta de nota gu mena yu zini re gani me toni ke hoha ro tihi se kuhu ba tobe mo ki
ho gi ku18
19

ma re bi
yo ku be18
19

ma bi reyo be ku

20

yo sa ke

20

yo ke sa21

yo ne ra21

yo ra ne22

ra ni yo22

ra yo ni23

ru ma ke

23

ru ke ma

Language and Speech

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

ka ta tika tu teka zo ku
ki ro kusi go tosu ga tata ti batu mo rito na rina ka mana ga reno ko ri
ha si raha ya ri
hi ka ri
hi ri tuhu ku sihu so ku
mi ka ta
mi ya komu ka simu su meyo so kure ki si

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

ko so kuka takiko no hake ma ri
si ki sosu te ki
to ri kota ma gote ga rane mu kena ra kuno ha rahi ka geha na wa
hi ga sa
ha ta tihi ra meha ka ma
ma ru mima do rimo to dema mo noya su ri
ra re tuPhoneme#    Real word#    Real word

PhonemeHigh bi-mora and low word frequencies

High bi-mora and high word frequencies

Three mora CVCVCV- structured real words used for Experiment 5

Appendix 5

1
2
3
4
5
6
7
8
9
10
11
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#    Real word
ka yu miko yu bi
ko gu mako be yasi gu sa
su ha date nu kite zi nato da na
ne wa zane re reno do kaha re giha da gi
hi ke mehi ne ri
hi gu reho ro bima yo kemi so gimo ro temo gu saya mo meri za yaPhonemeLow bi-mora and low word frequencies

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Japanese mental syllabaryLanguage and Speech