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Spoken Language Processing

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Presentation on theme: "Spoken Language Processing"— Presentation transcript:

0 Keiko Kuriyama (Randolph College) Jeri J. Jaeger (SUNY/Buffalo)
The Mora or the Segment?/ Investigating the Basic Unit of Spoken Language Processing Through SOT Data in Japanese Keiko Kuriyama (Randolph College) Jeri J. Jaeger (SUNY/Buffalo)

1 Spoken Language Processing
Speech production Speech perception Metalinguistic manipulations (Cutler 2002: 275)

2 Previous Studies Speech perception, metalinguistic experiments
evidence for the mora Speech production experiments less clear

3 Current Study Methodology: tongue twister experiment
Material: 8 well known traditional TTs (Experiment 1) & 10 short TTs (Experiment 2) Subjects: 17 Japanese adult speakers Number of SOTs: 115 (Experiment 1) 104 (Experiment 2)

4 Phonological Units Involved in Japanese SOTs
1) Phonetic features 2) Segments (consonants or vowels) 3) CV moras (a mora (body)) 4) Non-syllabic moras 5) Syllabic vs. non-syllabic moras4 6) Mora/syllables 7) Rhymes 8) Syllables 9) Pitch-accents

5 Experiment 1-Example of an Unambiguous Error
TT#1 ka.e-ru pyo-ko pyo-ko mi pyo-ko pyo-ko a-wa-se-te pyo-ko pyo-ko mu pyo-ko pyo-ko (frog, onomatopoeia for frog’s jumping, three, jumping, all, jumping, six, jumping) Error#1 ka.e-ru pyo-ko pyo-ko mi po-ko po-ko..mi pyo-ko pyo- ko a-wa-se-te pyo-ko pyo-ko mu pyo-ko pyo-ko ka.e-ru….(AF#3)

6 Experiment 1-Example of an Unambiguous Error
TT#2 na-ma-mu-gi na-ma-go-me na-ma-ta-ma-go (raw wheat, raw rice, raw eggs) Error#2 na-ma-mu-gi na-ma-go-me na-ma-ta-ma-go, na-ma mu-gi na-mi….na-ma-go-me na-ma-ta-ma-go..(AM#7)

7 The Number of Unambiguous Errors in Experiment 1
feature Con- sonat vowel mora Mora/ syllable pitch-accent total 4 28 2 1 8 43

8 Experiment 1-Example of Ambiguous Errors
TT#3 A-ka-ma-ga-mi a.o-ma-ki ga-mi ki-ma-ki-ga-mi (red rolled paper, blue rolled paper, yellow rolled paper) Error#3 a-ka-ma ki-ga-mi a.o-ma-ki-ga-mi ki-ma-ki-ga-mi, a a-ka-ma-ki-ma..a-ka-ma-ki-ga-mi a.o-ma-ki-ga-mi b ki-ma-ki-ga-mi, a.o…a-ka-ma-ki-ga-mi a.o-ma-ki-ga- mi…(AF#2)

9 Experiment 1-Example of Ambiguous Errors
TT#4 Pa.n ka-be (bread, wall) Error#4 a b c ka.n-pa-ge, ka.n-pa-be, pa.n-ka-be, pa.n-ka-be, pa.n-ka-be (AF#2)

10 The Majority Rules Method
Error #4 a b c ka.n pa-ge, ka.n pa-be, pa.n ka-be, pa.n ka-be, pa.n ka-be (AF#2) asegmental analysis, mora analysis bsegmental analysis, mora analysis csegmental analysis

11 Result of the ‘Majority Rules’ Method
Errors No. of instances (%) Segmental Analysis 95 (86.36%) Mora analysis 71 (64.45%) Syllable analysis 70 (63.63%) ______________________________________ 110 (100%)

12 The ‘General Principles’ Method
1) The Minimal Movement Principle (Laubstein 1987); if an error can be analyzed as a segment error it should be, because errors involving segments are the most common type of unambiguous phonological error. 2) The Repeated Phoneme Effect (Dell 1984); the repeated phoneme effect is the claim that repeated sounds in a speech plan are more likely to cause SOTs in adjacent segments than when there is no identical segment adjacent to the target and source.

13 Ambiguous Errors  the ‘General Principles’ Method
a b c d e ka.n-pa-ge, ka.n-pa-be, pa.n-ka-be, pa.n-ka-be, pa.n-ka-be (AF#2)

14 Result of Experiment 1 Errors by unit type subject AF#1 7 0 14 AF#2 1
consonant vowel feature mora mora/syll pitch accent total AF#1 7  0 14 AF#2 1 2 18 AF#3 12 3 5 20 AF#4 10 13 AF#5 8 23 AF#6 6 4 AM#7 67 32 115 % 58% 9% 3% 1% 28% 2% 100%

15 Result of Experiment 1 Errors by token subject AF#1 7 0 8 15 AF#2 18 3
consonant vowel feature mora mora/ syll pitch accent total AF#1 7  0 8 15 AF#2 18 3 1 2 24 AF#3 16 5 26 AF#4 20 AF#5 12 25 AF#6 4 14 AM#7 9 6 89 33 143 % 62% 8% 4% 1% 23% 100%

16 TTs Used in Experiment 2 1 ぱん こぶ pa.n ko-bu (bread, bump) 2 ぶん がま ばった
1 ぱん  こぶ pa.n ko-bu (bread, bump) 2 ぶん がま  ばった  bu.n ga-ma ba.t-ta (onomatopoeia: bee, frog, grasshopper) 3 かえる  ムー  ぴょこぴょこ  ka.e-ru mu.u pyo-ko pyo-ko (frog, onomatopoeia: cow cound, onomatopoeia: jumping frog) 4 かき くう きゃっきゃ ka-ki ku.u kya.k-kya (persimmon, to eat, onomatopoeia: monkey sound) 5 ねこ みゃー にゃんこ ne-ko mya.a nya.n-ko (cat, onomatopoeia: cat sound, kitten)

17 CV. X  CV TT#5 ぶん がま ばった bu.n ga-ma ba.t-ta
ぶん がま  ばった  bu.n ga-ma ba.t-ta (onomatopoeia: bee, frog, grasshopper) CV. X  CV Error Example) bu.n bu-ma ba.t-ta

18 Result of Experiment 2-type analysis
subject consonant vowel feature mora mora/syll syllable total AM#1 9 1 2 13 AM#2 6 10 AM#3 8 AM#4 5 AM#5 12 AF#1 4 24 AF#2 AF#3 AF#4 7 AF#5 69 104 % 66% 8% 1% 13% 100%

19 Result of Experiment 2-token analysis
subject consonant vowel feature mora mora/syll syllable total AM#1 29 1 5 2 37 AM#2 13 3 17 AM#3 20 23 AM#4 11 12 AM#5 21 24 AF#1 18 4 8 35 AF#2 9 AF#3 AF#4 7 AF#5 15 141 19 189 % 75% 4% 0.5% 9% 10% 1.5% 100%

20 Ratio between segmental errors vs mora+mora/syllable errors
Type Analysis Segments Mora Mora/syll ratio Ex 1 77 (67%) 33 (29%) 2.33:1 Ex 2 77 (74%) 26 (24%) 2.96:1

21 Ratio between segmental errors vs mora+mora/syllable errors
Token Analysis Segments Mora Mora/syll ratio Ex 1 101 (70%) 34 (24%) 2.97:1 Ex 2 149 (79%) 36 (19%) 4.13:1

22 Conclusions and future study
-segmental errors > mora + mora/syllable errors -universal underlying cognitive mechanism for speech production planning -a need for cross-linguistic study (Bilingual and L2 speakers of Japanese)


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