About Singaporean English
Singapore is a small city state of some 5.7 million people, of whom 4 million are citizens and permanent residents, according to the latest census figures released on the government’s website (https://www.singstat.gov.sg/). Nearly one million residents are 60 years of age or older, constituting 22% of the population. It is a multilingual country. Since the founding of Singapore as a British crown colony in 1819, most of the early immigrants hailed from southeastern China, southern India, Malaysia and the surrounding Riau Islands of Indonesia.29 When it gained independence in 1965, Singapore recognized four official languages, reflecting the origins of most of its immigrants: Chinese (Mandarin), Malay, Tamil, and English, with Malay having the additional title of national language, and English that of working language. For Chinese Singaporeans, in addition to Mandarin, there are mutually unintelligible dialects, the major ones being Hokkien, Teochew and Cantonese. Since the dialects share a common grammatical and lexical core,30 we group them together as a single language. At the present time, according to the government’s census survey, most Singaporeans are multilingual, with English as the dominant home language for nearly half of the households, and as the common lingua franca. Due to the constant contact with the local languages, the English language in Singapore has undergone extensive lexical and grammatical change, incorporating words (tau ‘soy’ from Chinese; atas ‘arrogant’ from Malay) and grammatical features from the local languages (one as a particle for emphasis).31 Despite the fact that it is the native language of a sizable segment of the population, Singaporean English has not reached the level of register differentiation as American or British English.32
The Language Samples
Our language samples are verbatim transcripts of the recordings of free-flowing speech by participants in a cohort study of ageing and mental health among Singaporeans who are 60 years of age or older. The aims and methods of the cohort study have been described elsewhere.8 Here, we describe how language data are processed. Language sampling is entirely voluntary. The recording took place in a normal office setting, with small digital recorders. Participants were told to talk about any topic for up to 20 minutes, in a language that they felt most comfortable with. They were aware that they were being recorded. Interviewer participation was kept to the minimum to allow subjects to plan their speech as free from intervention as is practical. Words uttered by interviewers were removed from the dataset.
We used the Stanford part-of-speech tagger to assign parts of speech to the words in our dataset. The tagger uses the Penn Treebank tagset, shown below:12,13
1. CC Coordinating conjunction
2. CD Cardinal number
3. DT Determiner
4. EX Existential there
5. FW Foreign word
6. IN Preposition or subordinating conjunction
7. JJ Adjective
8. JJR Adjective, comparative
9. JJS Adjective, superlative
10. LS List item marker
11. MD Modal
12. NN Noun, singular or mass
13. NNS Noun, plural
14. NNP Proper noun, singular
15. NNPS Proper noun, plural
16. PDT Predeterminer
17. POS Possessive ending
18. PRP Non-possessive pronoun (personal pronoun)
19. PRP$ Possessive pronoun
20. RB Adverb
21. RBR Adverb, comparative
22. RBS Adverb, superlative
23. RP Particle
24. SYM Symbol
25. TO Infinitival to
26. UH Interjection
27. VB Verb, base form
28. VBD Verb, past tense
29. VBG Verb, gerund or present participle
30. VBN Verb, past participle
31. VBP Verb, non-3rd person singular present
32. VBZ Verb, 3rd person singular present
33. WDT Wh-determiner
34. WP Wh-pronoun
35. WP$ Possessive wh-pronoun
36. WRB Wh-adverb
The same tag TO is used for tagging to as preposition (to school) and as infinitival marker (to go). We separate the two functions, and use TO for the infinitival to only. We also introduced two tags, SFP for sentence-final particle (Ok lah) and FRG for fragments, which are common in unprepared speech (fr- fragments).
When tagging Singaporean English materials, the Stanford tagger’s success rate is about 85%. Part of the reason for the low success rate is the frequent use of words which are unique to Singaporean English, including foreign words (tau huay ‘soy pudding’), and English words that have developed local uses or meanings. Consider one as an example. It is a cardinal number (one school) or a pronominal (last one). These are tagged as one_CD and one_NN, respectively. In addition to these two uses, one is also used in Singaporean English as a sentence-final particle to express emphasis:
My daughter is very active one
‘My daughter is very ACTIVE!’
The Stanford tagger tags one as a cardinal number here. To ensure accurate part-of-speech assignment, the tagged words were vetted by a separate group of student research assistants who were trained in formal linguistics at the National University of Singapore.
Three sample texts are shown below.
Extract 1 (64, male, cognitively healthy)
I came from a a very poor family. Um I grew up in er, you know, in those days where Singapore is a slum. So I’ve witnessed uh riots. I’ve witnessed er curfew. And er then I’ve also experience, I’ve experienced when er, you know, just sharing one bowl of tau huay for 10 person in the family you know and also to the the extreme is er just er plain rice and then with a sauce and oil and sauce, you know. Sometimes this goes on for weeks ah.
I_PRP came_VBD from_IN a_DT a_DT very_RB poor_JJ family_NN ._. Um_UH I_PRP grew_VBD up_RP in_IN er_UH ,_, you_PRP know_VBP ,_, in_IN those_DT days_NNS where_WRB Singapore_NNP is_VBZ a_DT slum_NN ._. So_RB I_PRP ’ve_VBP witnessed_VBN uh_UH riots_NNS ._. I_PRP ’ve_VBP witnessed_VBN er_UH curfew_NN ._. And_CC er_UH then_RB I_PRP ’ve_VBP also_RB experience_VBP ,_, I_PRP ’ve_VBP experienced_VBN when_WRB er_UH ,_, you_PRP know_VBP ,_, just_RB sharing_VBG one_CD bowl_NN of_IN tau_FW huay_FW for_IN 10_CD person_NN in_IN the_DT family_NN you_PRP know_VBP and_CC also_RB to_IN the_DT the_DT extreme_NN is_VBZ er_UH just_RB er_UH plain_JJ rice_NN and_CC then_RB with_IN a_DT sauce_NN and_CC oil_NN and_CC sauce_NN ,_, you_PRP know_VBP ._. Sometimes_RB this_DT goes_VBZ on_RB for_IN weeks_NNS ah_SFP ._.
Extract 2 (66, male, diagnosed with amnestic MCI)
why I felt that the secular practice of meditation and in in in this instance we are talking about mindfulness practice, which is really an approach, er a particular approach to meditation, can be helpful to everyone, is because er the teachings have been made secular, with hardly any reference to its religious origin, although we would mention that the approach er is founded on the b the the Buddha's teachings of meditation.
why_WRB I_PRP felt_VBD that_IN the_DT secular_JJ practice_NN of_IN meditation_NN and_CC in_IN in_IN in_IN this_DT instance_NN we_PRP are_VBP talking_VBG about_IN mindfulness_NN practice_NN ,_, which_WDT is_VBZ really_RB an_DT approach_NN ,_, er_UH a_DT particular_JJ approach_NN to_IN meditation_NN ,_, can_MD be_VB helpful_JJ to_IN everyone_NN ,_, is_VBZ because_IN er_UH the_DT teachings_NNS have_VBP been_VBN made_VBN secular_JJ ,_, with_IN hardly_RB any_DT reference_NN to_IN its_PRP$ religious_JJ origin_NN ,_, although_IN we_PRP would_MD mention_VB that_IN the_DT approach_NN er_UH is_VBZ founded_VBN on_IN the_DT b_FRG the_DT the_DT Buddha_NN 's_POS teachings_NNS of_IN meditation_NN ._.
Extract 3 (76, female, diagnosed with nonamnestic MCI)
Erm I had my nursing training in UK, Chesterfield. During my three years there I was uh very well treated. Uh I was a happy during my training time. I enjoy my training, I have very good colleagues and nursing other other higher nursing staff. Mmm I met my husband on my third year of my training. Er we got married after my after I passed my after my finished my my training, then I work in UK for three years, then later we came home. I have I have my daughter in UK. So later on we decide to to come home to Singapore.
Erm_UH I_PRP had_VBD my_PRP$ nursing_NN training_NN in_IN UK_NNP ,_, Chesterfield_NNP ._. During_IN my_PRP$ three_CD years_NNS there_RB I_PRP was_VBD uh_UH very_RB well_RB treated_VBN ._. Uh_UH I_PRP was_VBD a_DT happy_JJ during_IN my_PRP$ training_NN time_NN ._. I_PRP enjoy_VBP my_PRP$ training_NN ,_, I_PRP have_VBP very_RB good_JJ colleagues_NNS and_CC nursing_NN other_JJ other_JJ higher_JJR nursing_NN staff_NN ._. Mmm_UH I_PRP met_VBD my_PRP$ husband_NN on_IN my_PRP$ third_JJ year_NN of_IN my_PRP$ training_NN ._. Er_UH we_PRP got_VBD married_VBN after_IN my_PRP$ after_IN I_PRP passed_VBD my_PRP$ after_IN my_PRP$ finished_VBD my_PRP$ my_PRP$ training_NN ,_, then_RB I_PRP work_VBP in_IN UK_NNP for_IN three_CD years_NNS ,_, then_RB later_RB we_PRP came_VBD home_RB ._. I_PRP have_VBP I_PRP have_VBP my_PRP$ daughter_NN in_IN UK_NNP ._. So_RB later_RB on_RB we_PRP decide_VBP to_TO to_TO come_VB home_RB to_IN Singapore_NNP ._.
The tagged data are processed with Antconc, a common concordance tool used by corpus linguists.34
Two-tailed t-tests on age, year of education, languages spoken and talk time of the subjects who provide speech samples, and on the speech rate and concreteness score data are performed on SPSS v.27.