Contrastive analysis theory

1 Introduction

Narrowly defined, contrastive analysis investigates the differences between pairs (or small sets) of

languages against the background of similarities and with the purpose of providing input to applied

disciplines such as foreign language teaching and translation studies. With its largely descriptive focus

contrastive linguistics provides an interface between theory and application. It makes use of theoretical

findings and models of language description but is driven by the objective of applicability. Contrastive

studies mostly deal with the comparison of languages that are ‘socio-culturally linked’, i.e. languages whose

speech communities overlap in some way, typically through (natural or instructed) bilingualism.

2 Contrastive analysis and foreign language teaching

Different approaches to the phenomenon of language, different linguistic theories and schools of thought

influence our methods of teaching. Structural linguistic and the behaviouristic movement in psychology

resulted in the audio lingual method. The transformational approach, with its stress on the analytical

element in language learning, reintroduced rational, cognitive methods, but regardless of our view of

language, we must somehow solve a whole series of problems in the process of teaching a foreign language.

One of these problems is the relationship between the L1 (the learner’s native language) and L2 (the

language to be learned).

Contrastive analysis is the systematic study of a pair of languages with a view to identifying their structural

differences and similarities. Contrastive Analysis was extensively used in the 1960s and early 1970s as a

method of explaining why some features of a Target Language were more difficult to acquire than others.

According to the behaviourist theories, language learning was a question of habit formation, and this could

be reinforced by existing habits. Therefore, the difficulty in mastering certain structures in a second

language depended on the difference between the learners' mother language and the language they were

trying to learn. The theoretical foundations for what became known as the Contrastive Analysis Hypothesis

were formulated in Lado’s Linguistics across Cultures (1957). In this book, Lado claimed that "those

elements which are similar to the learner's native language will be simple for him, and those elements that

are different will be difficult". While this was not a novel suggestion, Lado was the first to provide a

comprehensive theoretical treatment and to suggest a systematic set of technical procedures for the

contrastive study of languages. This involved describing the languages (using structuralise linguistics),

comparing them and predicting learning difficulties acquisition, January 25th 2011). Thus, the languages

comparison is aimed at assisting language learning and teaching. The goals of Contrastive Analysis can be

stated as follows: to make foreign language teaching more effective, to find out the differences between the

first language and the target language based on the assumptions that: (1) foreign language learning is based

on the mother tongue, (2) similarities facilitate learning (positive transfer), (3) differences cause problems

(negative transfer/Interference), (3) via contrastive analysis, problems can be predicted and considered in

the curriculum. However, not all problems predicted by contrastive analysis always appear to be difficult

for the students. On the other hand, many errors that do turn up are not predicted by contrastive analysis.

Larsen, et al (1992: 55) states “predictions arising from were subjected to empirical tests. Some errors it did

predict failed to materialize, i.e. it over predicted.” This prediction failure leads to the criticism to the

Contrastive Analysis hypothesis.

The criticism is that Contrastive Analysis hypothesis could not be sustained by empirical evidence. It was

soon pointed out that many errors predicted by Contrastive Analysis were inexplicably not observed in

learners' language. Even more confusingly, some uniform errors were made by learners irrespective of their

L1. It thus became clear that Contrastive Analysis could not predict learning difficulties, and was only

useful in the retrospective explanation of errors. These developments, along with the decline of the

behaviourist and structuralise paradigms considerably weakened the appeal of Contrastive Analysis.

Fisiak (1981: 7) claims that Contrastive Analysis needs to be carried out in spite of some shortcoming

because not all Contrastive Analysis hypotheses are wrong. To overcome the shortcoming of contrastive

analysis, it is suggested that teachers accompany contrastive analysis with error analysis. It is carried out by

identifying the errors actually made by the students in the classroom. Contrastive Analysis has a useful

explanatory role. That is, it can still be said to explain certain errors and mistakes. He further explains “…

error analysis as part of applied linguistics cannot replace Contrastive Analysis but only supplement it.”

Schackne (2002) states “research shows that contrastive analysis may be most predictive at the level of

phonology and least predictive at the syntactic level.”

A counter-theory was error analysis, which treated second language errors as similar to errors encountered

in first language acquisition, or what the linguists referred to as "developmental errors." By the early 1970s,

this contrastive analysis theory had been to an extent supplanted by error analysis, which examined not only

the impact of transfer errors but also those related to the target language, including overgeneralization

(Schackne, 2002).

3 Conclusion

We may conclude that the aim of contrastive studies is not only a better understanding of the linguistic

structure, but also applied deductions, meant to raise the entire teaching activity above the empirical and

occasional practice, to outline fundamental teaching programs based on the scientific knowledge of the

language. Contrastive analysis has laid the emphasis on error analysis as a way to study the difficulties

encountered by foreign – language learners. The findings of such studies can be very helpful in setting up

teaching devices. Contrastive analysis and error analysis are complementary to one another, in the sense that

the results obtained and the predictions made by the contrastive studies are to be checked up and corrected

by the results obtained in the error analysis.

LEXICON ADAPTATION FOR LVCSR: SPEAKER IDIOSYNCRACIES, NON-NATIVE SPEAKERS, AND PRONUNCIATION CHOICE

Wayne Ward, Holly Krech, Xiuyang Yu, Keith Herold, George Figgs, Ayako Ikeno, Dan Jurafsky

Center for Spoken Language ResearchUniversity of Colorado, Boulder

William Byrne

Center for Language and Speech ResearchThe Johns Hopkins University

ABSTRACT

We report on our preliminary experiments on building dy- namic lexicons for native-speaker conversational speech and for foreign-accented conversational speech. Our goal is to build a lexicon with a set of pronunciations for each word, in which the probability distribution over pronunciation is dy- namically computed. The set of pronunciations are derived from hand-written rules (for foreign accent) or clustering (for phonetically-transcribed Switchboard data). The dy- namic pronunciation-probability will take into account spe- cific characteristics of the speaker as well as factors such as language-model probability, disfluencies, sentence position, and phonetic context. This work is still in progress; we hope to be further along by the time of the workshop.

1. INTRODUCTION

Many ASR researchers have suggested the idea of a dy- namic lexicon: a lexicon with a large number of pronunci- ation variants whose probability is set dynamically accord- ing to various factors. ([1] inter alia). This paper is the preliminary description of our project to apply this idea to two domains: Switchboard (human-human native Ameri- can English telephone conversations) and Hispanic English (conversations in English between native Spanish speakers with varying levels of accent). Both of these domains are known to have high error rates, and pronunciation varia- tion is known to contribute to the difficulty of these tasks [2, 3, 4, 5].

The goal of this work-in-progress is to build a lexicon with a set of pronunciations for each word, in which the probability distribution over pronunciation is dynamically computed. The set of pronunciations are derived from hand- written rules (for foreign accent) or clustering (for phonetically- transcribed Switchboard data). The dynamic pronunciation- probability will take into account specific characteristics of

Thanks to the NSF for partial support of this research via award #IIS-9978025.

the speaker as well as factors such as language-model prob- ability, disfluencies, sentence position, and phonetic con- text.

Section 2 describes a preliminary experiment suggesting that a ‘dynamic lexicon’ is only useful if words have many pronunciations. Section 3 describes our preliminary work on automatically creating pronunciations. Section 4 reports on preliminary work on the foreign-accent accented data.

2. PILOT EXPERIMENT: DYNAMIC LEXICON WITH TWO PRONUNCIATIONS

Our first experiment was an oracle experiment designed to show whether having exactly two pronunciations for each of the 50 most frequent words in Switchboard, a very full pronunciation and a very reduced pronunciation, would im- prove recognition.

Our experiments were conducted using Sonic [6], a large vocabulary continuous speech recognition system with Viterbi decoding, continuous density hidden Markov models and trigram language models. Sonic’s acoustic models are decision- tree state-clustered HMMs with associated gamma proba- bility density functions to model state-durations. Our ex- periments used only the first-pass of the decoder, which consists of a time-synchronous, beam-pruned Viterbi token- passing search. Cross-word acoustic models and trigram language models are applied in this pass. This first exper- iment was run with an early version of Sonic, which hada WER of 42.9% on the 888-sentence Switchboard WS97-test set. (By comparison, WER on this test set in our current version of Sonic is 32.9%).

We used SRI’s Hub-5 language model, generously made available by Andreas Stolcke. We built our 39,198-word lexicon from the Mississippi State ISIP Switchboard lexi- con. Since this dictionary did not have every word in the LM, we used the CMU dictionary as a resource for any words that were in the LM but were not in the ISIP lexicon. We also included 1658 compound words (‘multiwords’), of which 1393 were not in the ISIP or CMU lexicons. So for

these 1393 we included two pronunciations, full (by con- catenating the pronunciations of the consituents words) and reduced (hand-written). The average number of pronuncia- tions per word is 1.13.

We built 2 versions of this lexicon, which differed only in the pronunciations of the top 50 words. In the ‘single- pron’ lexicon, we allowed only one pronunciation for the most frequent 50 words. In the ‘two-pron’ lexicon, we in- cluded two pronunciations for each of these words, a canon- ical pronunciation and a very reduced pronunciation, with equal probabilities. Finally, we created a test set from 4237Switchboard utterances which had been phonetically labeled [?, 7]. This allowed us to know, for each test utterance, whether the correct pronunciation of each word was canon- ical or reduced. From this we built a third dynamic lexicon, a ‘cheating’ or ‘oracle’ lexicon, which for each test set sen- tence only used the pronunciation that was present in the test set.

We then tested the three lexicons with and without re-training the acoustic models. Table 1 shows the results.

Models Lexicon WER Baseline Model single-pron 43.7Baseline Model oracle 41.8Retrained Models oracle 41.5Retrained Models two-pron 41.7

Table 1. Comparing lexicon performance on a 4237- utterance SWBD test set

Table 1 suggests that having two pronunciations rather than one for the 50 most-frequent words does in fact re- duce WER (by 2%, from 43.7% to 41.8%). But an oracle telling us which pronunciation to use (41.5% WER) was not significantly better than just putting in both pronunciations (41.7% WER). This suggests that two pronunciations is an insufficient number for any kind of dynamic lexicon to be useful. In essence, with only two pronunciations, the rec- ognizer was able to choose the correct pronunciation, even without a pronunciation probability.

As a result of this pilot, we determined that a dynamic lexicon would need to have large numbers of pronuncia- tions, more than we were thought was possible to correctly write by hand. In the next two sections, we discuss how we are building pronunciations by clustering and rule-writing.

3. SWITCHBOARD EXPERIMENT: BUILDING MORE PRONUNCIATIONS AND MAPS

3.1. Baselines

Before describing our clustering work, we describe our in- tended baseline for the SWBD experiments. This is a 5-step extract-align-count-prune-retrain algorithm generalized from [8]:

1. Extract observed alternate word pronunciations from the ICSI labeled data.

2. Align pronunciations with training data

3. Count number of times each pronunciation occurs

4. Prune pronunciations with low counts

5. Retrain acoustic models with alignments to new dic- tionary

6. (Evaluate WER on test set)

We will then build a slightly more advanced clustered version of the algorithm, in which pronunciations are clus- tered into broad classes (Vowel Front, Vowel Back, Vowel Reduced, Consonant Labial, Consonant Dorsal, Silence) be- fore accumulating counts. Then we keep at least one ex- ample of each broad class with sufficient count, before the align, prune, re-train and evaluate steps.

For example, the word that has 36 phone-level variant pronunciations; [dh ae] and [dh ae t] are the most frequent. It has 19 broad class variants, with [CC VF] and [CC VF CC] being the most frequent.

We have already aligned and counted pronunciations, both for phones and broad classes, and are currently work- ing on pruning and then retraining acoustic models.

3.2. Building broad-class maps

In addition to building pronunciations, we are creating a new kind of pronunciation feature based on canonical-to- surface mappings, relying on a database originally produced by Eric Fosler-Lussier that aligns canonical pronunciations with surface pronunciations from the ICSI phonetically la- beled data.

A mapping is a change or transduction from the canon-ical phone sequence to the surface phone sequence, con- taining a sequence of differing labels (of whatever length) anchored on each end by labels that are the same in both se- quences. For the maps, in addition to the 7 broad classes, 3 word positions, b(eginning), m(iddle) and e(nd) were used. For example, in the following map pattern the sequence to the left of is the canonical sequence, the sequence to the right is the surface sequence, and ”vb:e” represents a back vowel at the end of a word:

sil cc:b vb:e cc:b sil null vf cc

This algorithm has 4 steps:

1. Accumulate counts for all canonical-to-surface map- pings in the training data:

with and without word boundary info, with phones and with broad classes:

2. Prune low frequency maps3. Cluster maps by co-occurrence into classes which

will define speaker types

After computing counts from the training data, low fre- quency patterns were pruned to give the final set of map patterns. For each session side, the frequency of each of the patterns in the set was computed, including the frequency of each canonical string mapping onto itself. The patterns are currently being clustered to produce a set of classes with correlated pattern probabilities. These will define a set of speaker classes on the basis of the observed frequency of patterns. It is generally the case that relatively few patterns account for much of the data. For example, 19 broad class patterns account for about 50% of the sequence differences in the training data.

These derived speaker classes and their probability esti- mates will be used as features in the decision trees determin- ing the probabilities for alternate pronunciations of words.

4. DYNAMIC LEXICONS FOR SPANISH ACCENTED ENGLISH

4.1. The Hispanic-English corpus and test sets

We are using the conversational Hispanic-English corpus developed at Johns Hopkins University [9]. This database contains about 20 hours of telephone conversations in En- glish from 18 native Spanish speakers, 9 male and 9 female. All speakers were adults from South or Central America who had lived in the United States at least one year and had a basic ability to understand, speak and read English.

During the telephone conversations, the speakers com- pleted four tasks: picture sequencing, story completion, and two conversational games. For the picture sequencing task, participants received half of a randomly shuffled set of car- toon drawings and were asked to reconstruct the original narrative with their partner. For the story completion, par- ticipants were given two identical copies of a set of draw- ings depicting unrelated scenes from a larger narrative con- text and were asked to answer three questions: “What is going on here?, What happened before?, What is going to happen next?” The first conversational game, Scruples, in- volved reading a description of a hypothetical situation and trying to resolve the conflict or dilemma. For the second game, the speaker pairs were asked to agree on five profes- sionals to take along on a mission to Mars from a list of ten professions.

These data were divided into development, training and test sets according to speaker proficiency and gender. The development and test sets both include about 30,000 words; from four speakers in the test set, and two in the dev set, while the training set contains about 70,000 words from the remaining ten speakers, five male and five female (See Table 2). Speakers had been judged on proficiency scores based on a telephone-based, automated English proficiency test [10] We also listened to each speaker and rated their

accent as heavy, mid and light. We then combined the profi- ciency scores with our accent ratings to distribute speakers with heavy, mid and light accents evenly into the different data sets. A range of the degree of accentedness is thus rep- resented in each data set.

Set Gender Minutes WordsTraining 5 male, 5 female 546 69,926Dev 1 male, 1 female 176 29,474Test 2 male, 2 female 282 30,104

Table 2. Hispanic-English training and test set statistics

4.2. Baseline recognizer performance

We used the Sonic speech recognizer with our SWBD lexi- con and acoustic models to establish a baseline from a sys- tem trained on native American English on Hispanic-English speech. Our SWBD system, as described earlier, consists of a 39,000 word lexicon, the SRI Hub-5 language model, and SWBD acoustic models. On the development test set of 176 minutes of speech and 29,974 words, we achieved a base- line word error rate of 62%.

4.3. Pronunciation rules for Hispanic-English

We next created lexical variants on the basis of seven phono- logical rules (See list below). These rules represent com- mon characteristics of Spanish accented English, and they were determined by comparing literature about Spanish ac- cents [11] to the Hispanic-English database and selecting the most appropriate characteristics. The seven rules are:

1. epenthetic schwa added before words beginning in /s/, as inspeak [ax s p iy k];

2. past tense morpheme -ed pronounced /ax d/ following voiced

consonants, as in planned [p l ae n ax d]”;3. reduced schwa vowels pronounced as they are spelled, the

full vowel represented by the orthography, as in minimum[iy n iy m uw m]”;

4. the mid-high vowels /ih/ and /uh/ become the high vowels/iy/ and /uw/;

5. /s/ and /z/ in word fi nal position are deleted;6. the fricatives /sh/ becomes the affricate /ch/ in word initial

position, and7. the fricative /dh/ becomes the stop /d/.

Table 3 gives formal versions of the rules.While we have not yet tested whether these rules help

in improving recognition performance, we have analyzed some of the errors when the Switchboard recognizer is ap- plied to the Hispanic English dev set, yielding some anecdo- tal observations that relate to the rule set. First, final conso- nants tend to be deleted, especially /s/, /z/, /v/ and /t/, caus- ing substitutions of words with no final consonants, such as “know” for “not” and “how” for “have”. Our phonological rules account only for the deletion of /s/ and /z/. Second, the /dh/ fricative is pronounced as both /d/ and /s/, not just as the /d/ we indicate in our rules. Another fricative that is

1. s ax s / #2. d ax d / voiced C #

4. ih iyuh u

5. s 0 / #z 0 / #

3. ax aa / orthographic a ax eh / orthographic ’e’ ax iy / orthographic ’i’ axow / orthographic ’o’ ax uw / orthographic ’u’

axr er / orthographic ’er’

6. sh ch / #7. dh d

Table 3. Phonological Rules for Hispanic English

problematic is /f/, which is pronounced and recognized as/p/. Third, the softening of /b/ to a bilabial fricative causes substitution of words that have no stop consonant where the/b/ occurs, as in “busy” substituted with “easy”. Fourth, many of the reduced vowels are pronounced and recognized as full vowels, which we expected based on the third phono- logical rule. Finally, hesitations seem to be nasalized, with “nn” for “uh”, which causes the recognizer to substitute a short word beginning with a nasal, such as “no” or “not”, for these hesitations.

4.4. Applying pronunciation count-prune-retraining

We next use the phonological rules discussed above to at- tempt to build a better baseline system for Hispanic English. We use the 3-step algorithm first proposed by [12]:

apply phonological rules to the base lexicon, generat- ing a large number of pronunciations,forced-align against the training set to get pronuncia- tion countsprune low-probability pronunciations

Our base lexicon was the Switchboard lexicon described above , consisting of 39204 word tokens with 1.13 pro- nunciations per word type. We applied the 7 phonological rules in Section 4.3 to produce ’accented’ pronunciations, which were then merged with the base lexicon, and redun- dant forms were removed. The resulting augmented lexicon consisted of 96954 word tokens with 2.8 pronunciations per word type. Next, this augmented dictionary was aligned with the reference corpus data, giving us counts of the num- ber of times a particular pronunciation was choosen for a given word.

We are currently working on the pruning step. Once thatis complete, we will proceed to retraining the acoustic mod- els with the resulting dictionary. That will provide a ‘static lexicon’ baseline which we can then use to see the perfor- mance of our dynamic lexicon approach on the Hispanic- English data.

5. CONCLUSION

Our main result so far is that hand-writing very-reduced pro- nunciations for 50 frequent function words reduces word error rate even after using a lexicon with 1600 reduced- pronunciation multi-words, usually based on these same func- tion words. Our other results are still too preliminary to ad- mit of much conclusion, but we hope to have more results by September.

6. REFERENCES

[1] Eric Fosler-Lussier, Dynamic Pronunciation Models for Au- tomatic Speech Recognition, Ph.D. thesis, University of Cal- ifornia, Berkeley, 1999, Reprinted as ICSI technical report TR-99-015.

[2] Don McAllaster, Larry Gillick, Francesco Scattone, and Mike Newman, “Fabricating conversational speech data with acoustic models: A program to examine model-data mis- match,” in ICSLP-98, Sydney, 1998, vol. 5, pp. 1847–1850.

[3] Mitch Weintraub, Kelsey Taussig, Kate Hunicke-Smith, and Amy Snodgras, “Effect of speaking style on LVCSR perfor- mance,” in ICSLP-96, Philadelphia, PA, 1996, pp. 16–19.

[4] Murat Saraclar, Harriet Nock, and Sanjeev Khudanpur, “Pro- nunciation modeling by sharing gaussian densities across phonetic models,” Computer Speech and Language, vol. 14, no. 2, pp. 137–160, 2000.

[5] Dan Jurafsky, Wayne Ward, Zhang Jianping, Keith Herold, Yu Xiuyang, and Zhang Sen, “What kind of pronunciation variation is hard for triphones to model?,” in IEEE ICASSP-01, Salt Lake City, Utah, 2001, pp. I.577–580.

[6] Bryan Pellom, “Sonic: The university of colorado continu- ous speech recognizer,” Tech. Rep. TR-CSLR-2001-01, Cen- ter for Spoken Language Research, University of Colorado, Boulder, 2001, Revised April 2002.

[7] Steven Greenberg, “Speaking in shorthand — a syllable- centric perspective for understanding pronunciation varia- tion,” Speech Communication, vol. 29, pp. 159–176, 1999.

[8] Michael D. Riley, William Byrne, Michael Finke, Sanjeev Khudanpur, Andrei Ljolje, John McDonough, Harriet Nock, Murat Saraclar, Chuck Wooters, and George Zavaliagkos, “Stochastic pronunciation modeling from hand-labelled pho- netic corpora,” Speech Communication, vol. 29, pp. 209–224, 1999.

[9] W. Byrne, E. Knodt, S. Khudanpur, and J. Bernstein, “Is automatic speech recognition ready for non-native speech? a data collection effort and initial experiments in modeling conversational hispanic english,” in ESCA Workshop, 1998.

[10] Ordinate Corporation, “The phonepass test,” 1998.

[11] H. S. Magen, “The perception of foreign-accented speech,”Journal of Phonetics, vol. 26, pp. 381–400, 1998.

[12] Michael H. Cohen, Phonological Structures for Speech Recognition, Ph.D. thesis, University of California, Berke- ley, 1989.

Data Findings

In this section, I have to record my subject’s conversation with her friend. After finished

recording, I will then study her pronunciation and transcribe her sentences. In my study, I have to

detect the errors made by my subject and try to correct it with the right transcription.

A: Have you watched the latest movie of Twilight: Breaking Dawn?

B: Nope, I haven’t watched it yet. I’ve been busy with some work.

A: What are you busy with?

B: I have a lot of assignment to do first.

A: Cehh...Since when you became a studious nerd?

B: Since I have to submit it by next week?

A: Alright then, how’s your assignment going?

B: Pretty good I guess? Oh, by the way, was the Twilight good?

A: It was awesome! Taylor’s so hot I tell you!

B: Well I think Edward is more better than Taylor.

A: Whatever, if you insist. You can have your white face Edward.

B: Hello! He can shine under the sunlight!

A: Okay...So, do you want to know how the story ends?

B: No you idiot! You’ll ruin the surprise!

A: Okay fine! I won’t tell you. Anyway it’s better to watch it yourself.

B: Okay lahh... I better finish my assignment first.

A: Okay, bye.

B: Bye.

My subject is a 19 years old Kedahan-Malay who is studying Diploma in Early Childhood

Education in semester one.

Subject’s transcriptionCorrect

transcriptionComment

Have you watched the latest movie Twilight Breaking Dawn?

Have həv

you juː

watched wɒtʃt

the də ðəthere is no /ð/ sound in Malay

language

latest ˈleɪtɪst

movie ˈmuːvi

Twilight ˈtwɪlaɪt ˈtwaɪlaɪtsubject pronounce the word as

it spelled

Breaking ˈbreɪkɪŋ

Dawn daʊn dɔːnsubject confused the word

‘dawn’ with ‘down’

What are you busy with?

What ˈwɒt

are ɑː

you juː

busy ˈbɪzi

with wɪf wɪðthe /ð/ sound is pronounce

as /f/

Cehh...Since when you become a studious nerd?

Cehh [Cehh] -influence by the typical Malay

conversation

Since sɪns

when wen

you juː

became bɪˈkeɪm

a eɪ

studious ˈstjuːdɪəs

nerd nɜːrd nɜːd

subject stresses on the /r/

sound since the word is

spelled it that way

Alright then, how’s your assignment going?

Alright ɔːlˈraɪt

then den ðen

the /ð/ sound is pronounce

as /d/ sound and the /en/ sound

is pronounce long when it is

supposed to be short

how’s haʊs haʊzsubject pronounce the /z/

sound like a /s/ sound

your jɔː

assignment əˈsaɪnmənt

going ˈɡəʊɪŋ

It was awesome! Taylor’s so hot I tell you!

It ɪt

was wəs wəzthe /z/ sound is pronounce as a

/s/ sound

awesome ˈɔːsəm

so sɔː ˈsəʊ

subject pronounce the word as

it spelled, the /əʊ/ sound like a

/ɔː/ sound

hot hɒt

I ˈaɪ

tell tel

you juː

Whatever, if you insist. You can have your white face Edward.

Whatever wɒtˈevə

if ɪf

you juː

insist ɪnˈsɪst

You juː

can kən

have həv

your jɔː

white waɪt

face feɪs

Okay...So, do you want to know how the story ends?

Okay ɔːˈkeɪ əʊˈkeɪthe / əʊ/ sound is prounce

as /ɔː/ sound

So sɔː ˈsəʊ

subject pronounce the word as

it spelled, the /əʊ/ sound like a

/ɔː/ sound

do duː

you juː

want wɒnt

to tuː

know nəʊ

how ˈhaʊ

the ðə

story ˈstɔːri

ends ends endzthe /z/ sound is pronounce like

a /s/ sound

Okay fine! I won’t tell you. Anyway it’s better to watch it yourself.

Okay əʊˈkeɪ

fine faɪn

I ˈaɪ

won’t wəʊnt

tell tel

you juː

Anyway ˈeniweɪ

it’s ɪts

better ˈbetə

to tuː

watch wɒtʃ

it ɪt

yourself jɔːˈsɛlf

Okay, bye.

Okay ˌəʊˈkeɪ

bye baɪ

Analysis and Discussion of Data Findings

Pronunciation refers to the utterances of words in spoken language. While “Correct

pronunciation” refers to how to utter the words in the right sounds of the targeted language other

than the mother tongue. The right pronunciation usually refers to how the native speakers of the

targeted language say or utter the words. With good enunciation and pronunciation, someone

does can understand what the other person is saying. There are some common problems in

pronunciation in an ESL classroom and one of them is mother tongue interference.

When learning the second language (L2), the person with the knowledge of the first language

(L1) sometimes will apply the rules of their first language in the second Language. This scenario

is popular among the Malaysian people. In Malaysia we live with three different races and

cultures which is Malay, Chinese, and Indian people. Sometimes, people who came from English

speaking background cannot pronounce the English words correctly because they are used to the

Malaysian English (Manglish). This is where the Malaysian applies the Bahasa Melayu rules in

English language. When speaking in English language among the Malaysian people, the

Malaysian tends to put the –lah at the end of the conversations. For example “okaylah”, “no lah”

and “see lah”. Sometimes Malaysian also tends to put repetition in their English language. They

apply the rules of first language in the second Language, such as “don’t play-play” (jangan main-

main) and together-gather (bersama-sama). They tend to apply the rules of Malay language in the

English language.

Problem in pronunciation also occur because a specific sound in the English language do not

exist in the mother tongue. The students need to be helped to hear the sounds and help they

understand how the sound is produce and given a lot of practice to make the perfect

pronunciation of the words. It will become a lot easier if the students know the phonemic

alphabet, but in English some words does not sounds exactly like how they are spelt. For

example “ough”, cough, although, through, bough, rough, etc. These chosen words do not

produce the same sound although they kind of have the same spelling at the back of the words.

Another prove of mother tongue is the biggest interference in the pronunciations are, when we

look at the Chinese people which in their Chinese language system doesn’t have the “r” sound.

When learning English language as well as other language, the most common problem they faces

was, they can’t pronounce the words that have the “r” sound in it. For example, in Bahasa the

word “rokok” they tend to pronounce it “lokok”. In English language the word “rabbit” turns into

“labbit”. For the Indian people, in their language system they pronunciation of the “r” sound is

very thick. That’s why when they pronounce in English word that has the “r” sound they tend to

stress it. This way of pronunciation is not right although we might understand the word that they

are saying.

The next problem in pronunciation in an ESL classroom is the low self-esteem when speaking

English. Since it is not their first language the second Language learners that are not used to

speak in English will feel shy when speaking in English because of afraid of making mistakes.

This is due to improper training and lack of exposure to English language. Furthermore, the

tutors themselves here in Malaysia are not the native speakers. Sometimes tutors tend to

pronounce wrongly and students will tend to follow. Some of second Language students are

passive students. This makes them hard to achieve perfection when speaking in English. These

low self-esteem problems happen when the second Language learners tend to make mistake

when speaking. The common mistakes that they make when speaking is they tend to stress

individual words incorrectly. This problem can be fixed by hearing correctly and pronounce it

correctly by using the guide in the dictionary. Second Language students also tend to stress the

words in a sentence wrongly. In English language, by stressing different words in a sentence, we

can actually change the meaning of the sentence. If you stress the wrong word, the listener might

get the wrong message. Pronunciations cover on word stress, sentence stress and intonation.

By learning all of these, second language learners can actually pronounce correctly. There are

eight common pronunciation features that second language learners must achieve in order to

pronounce correctly that is voicing, aspiration, mouth position, intonation, linking, vowel length,

syllables and specific sounds. By achieving all of these, learners can actually boost their self-

esteem in speaking English.

References

Gast, V., Contrastive analysis . Retrieved January 14, 2013 from http://www.personal.uni-

jena.de/~mu65qev/papdf/CA.pdf

Mihalache, R., Contrastive analysis and Error Analysis - Implications for Teaching of English.

Retrieved January 14, 2013 from

http://www.academia.edu/422410/CONTRASTIVE_ANALYSIS_AND_ERROR_ANAL

YSIS-IMPLICATIONS_FOR_THE_TEACHING_OF_ENGLISH

Rustipa, K., Contrastive Analysis, Error Analysis, Interlanguage and the Implication to

Language Teaching . Retrieved January 14, 2013 from

http://www.polines.ac.id/ragam/index_files/jurnalragam/paper_3%20apr_2011.pdf

Lexicon Adaptation for LVCSR: Speaker Idiocyncracies, non-native speakers, and pronunciation

choice, Retrieved February 1, 2013 from

http://www.stanford.edu/~jurafsky/pmla.pmod.pdf