ISCA Archive

SLTU-2014, St. Petersburg, Russia, 14-16 May 2014

http://www.isca-speech.org/archive

RECENT PROGRESS IN DEVELOPING GRAPHEME-BASED SPEECH RECOGNITION
FOR INDONESIAN ETHNIC LANGUAGES:JAVANESE, SUNDANESE, BALINESE AND BATAKS
Sakriani Sakti, Satoshi Nakamura
Augmented Human Communication Laboratory,
Graduate School of Information Science,
Nara Institute of Science and Technology, Japan{ssakti,s-nakamura}@is.naist.jp

ABSTRACTWith the advent of globalization, multilingualism in Indonesia gradually faces a state of catastrophe. Currently among
726 ethnic languages spoken in Indonesian archipelago, 146
are endangered. Several projects have been initiated for cultural preservation which can prevent the endangered language
from being lost. Nevertheless, the available technology that
could support communication within indigenous communities, as well as with people outside the community, is still
very rare in Indonesia. Speech translation technology is oneof the technologies that may help indigenous communities
in Indonesia to overcome language barrier and cross cultural
gap as well as to face globalization. Our long-term goal isto establish an infrastructure of speech translation systemfrom ethnic languages to English/Indonesian, and this paper presents recent progress of data resources collection andspeech recognition system development for four Indonesianmajor ethnic languages: Javanese, Sundanese, Balinese andBataks.
Index Terms— Language preservation, Indonesian ethnic languages, speech data collection, speech recognition system.
1. INTRODUCTION
The global, borderless economy and information communication technologies have a great impact in the way of communication. People have to be able to communicate well withothers who speak different language. However, on the other
hand, intangible cultural expressions, such as oral traditions
and literature, are fragile and easily lost. There exist several international projects (i.e., UNESCO’s ICT4ID project in2004-2005) that have been initialized to utilize the use of information and communication technology (ICT) for culturalpreservation for preventing them from being lost. Nevertheless, the available technology that could support communication between elders and younger people within indigenous

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communities, as well as with people outside the community,
is still limited. As a result, indigenous communities may still
face isolation due to language and cultural barriers.
Indonesia is reported to be one of the most religiously, linguistically, and ethnically diverse regions of the world [1, 2,
3]. It is an archipelago comprising approximately 17500 islands inhabited by hundreds of ethnic groups with more than
241 million people (based on Census 2012). Different ethnic groups speak various different languages. Approximately,
there are 300 ethnic groups living in 17,508 islands, that speak
726 native languages [4].
One of the bridges that binds the people together inIndonesia is the usage of Bahasa Indonesia, the national
language. It is a unity language formed from hundreds oflanguages spoken in the Indonesian archipelago, which wascoined by Indonesian nationalists in 1928 and became a symbol of national identity during the struggle for independence
in 1945. Compared to other languages, which have a highdensity of native speakers, only small proportion of Indonesia’s large population speak Bahasa Indonesia as a mothertongue while the great majority of people speak it as a second
language with varying degrees of proficiency.
Although the phenomena of using the unity language
could help the Indonesian people to face the globalization,
multilingualism in Indonesia gradually faces a state of catastrophe. Currently among 726 ethnic languages, only thirteen still have a million or more speakers, accounting for
69.91% of the total population, including: Javanese, Sundanese, Malay, Madurese, Minangkabau, Bataks, Bugisnese,
Balinese, Acehnese, Sasak, Makasarese, Lampungese, andRejang [5]. Moreover, of these 13 languages, only 7 languages have presence on the Internet [6]. However, theremaining 713 languages have a total population of only 41.4
million speakers, and the majority of these have very small
numbers of speakers [7]. For example, 386 languages are
spoken by 5,000 or less; 233 have 1,000 speakers or less; 169
languages have 500 speakers or less; and 52 have 100 or less[8]. These languages are facing various degrees of language

SLTU-2014, St. Petersburg, Russia, 14-16 May 2014
endangerment [9].
The long-term goal is to establish an infrastructure ofspeech-to-speech translation system from ethnic languages toEnglish/Indonesian. This technology enables communication
between the first person who speaks in any language andthe other person can understands the meaning of the speech.
Therefore, speech translation technology is significant to indigenous communities in Indonesia to overcome language
barrier and cross cultural gap as well as to face globalization.
This paper presents recent progress of data resources collection and speech recognition system development for fourIndonesian major ethnic languages: Javanese, Sundanese,
Balinese and Bataks.
In the next section, we briefly describe the overview ofstandard Indonesian and four major Indonesian ethnic language characteristics. The existing Indonesian resources will
be described in Section 3, and the current progress in developing speech corpora for Indonesian ethnic languages will be
described in Section 4. Then, Section 5 describes the detailsof speech recognition system development. Finally, conclusions are drawn in Section 6.
2. INDONESIAN AND ETHNIC LANGUAGESCHARACTERISTICS
2.1. Indonesian National Language
The official/national Indonesian language, so-called Bahasa
Indonesia, is a unity language formed from hundreds of languages spoken in the Indonesian archipelago. Although theIndonesian language is infused with highly distinctive accentsfrom different ethnic languages, there are many similarities
in patterns across the archipelago. Modern Indonesian isderived from the literary of the Malay dialect, which was
the lingua franca of Southeast Asia[10]. Thus, it is closely
related to Malay spoken in Malaysia, Singapore, Brunei,
and some other areas. Concerning the number of speakers,
today Malay-Indonesian ranks around sixth in size among
the world’s languages. The only difference is that Indonesia (which was a Dutch colony) adopted the Van Ophuysen
orthography in 1901, while Malaysia (which was a Britishcolony) adopted the Wilkinson orthography in 1904. In
1972, the governments of Indonesia and Malaysia agreed
to standardize the ”improved” spelling, which is now in effect on both sides. Even so, modern Indonesian and modern
Malaysian are as different from one another as are Flemish
and Dutch.
The standard Indonesian language is continuously being
developed and transformed to make it more suitable to thediverse needs of a modernizing society. Many words in the
vocabulary reflect the historical influence of various foreigncultures that have passed through the archipelago. It has borrowed heavily from Indian Sanskrit, Chinese, Arabic, Portuguese, Dutch, and English. Although the earliest records in47

Malay inscriptions are syllable-based written in Arabic script,
modern Indonesian is phonetic-based written in Roman script.
It use only 26 letters as in the English/Dutch alphabet. The
Indonesian phoneme set is defined on the basis of an Indonesian grammar text [11]. A full phoneme set contains a total of
33 phoneme symbols, which consist of 10 vowels (including
diphthongs), 22 consonants.
2.2. Ethnic Languages
On the other hand, some of ethnic groups in Indonesia still use
their own transcription in daily life. As the four major ethnic groups in Indonesia, Javanese, Sundanese, Balinese andBataks are counted in that category. Each of these four major Indonesian ethnic languages are further discussed in thefollowing:
1. Javanese
Javanese language had a long history of its development. Based on the evidence in the form of inscriptions
and paleography, the earlier stage of Javanese script
was started before the eight century [12]. Javanesetranscription is called Aksara Hanacaraka. It consistsof 20 basic scripts called Carakan, including 20 consonants and 1 vowel. Letter is called Nglegena ’naked’
because it has not had any Sandhangan or clothes
that could make them into another vowel sounds. To
make Javanese vowels have another sound, it needs
an additional tool called Sandhangan. Fig. 1(a) shows
Javanese script1 . Currently, Hanacaraka is already included in Unicode (A980-A9DF).

2. SundaneseSundanese has been written in a number of scripts.
Pallawa or Pra-Nagari was first used in West Java towrite Sanskrit from the fifth to eighth centuries, andfrom Pallawa was derived Sunda Kuna or Old Sundanese which was used in the Sunda Kingdom from the14th to 18th centuries [13]. Modern Sunda transcription called Aksara Sunda. Aksara means transcription
in Indonesia. Similar with Hanacaraka, Aksara Sunda
shown in Fig. 1(b) also has basic alphabets, vowels
and punctuation to change phoneme and basic punctuation2 . Basic letters in Aksara Sunda, has also beenregistered in Unicode (1B80-IBBF).

3. Balinese
The Balinese script is without doubt derived from Devanagari and Pallava script from India. The shape of thescript shows similarities with southern Indian scripts
like Tamil. The concept of syllable also found in other
1 The official site of Aksara Jawa. http://hanacaraka.fateback.com/
2 http://en.wikipedia.org/wiki/Sundanese alphabet

SLTU-2014, St. Petersburg, Russia, 14-16 May 2014
South/Southeast Asian scripts, such as the modern
Devanagari, Tamil, Thai, Lao, and Khmer scripts. Figure 1(c) shows the Balinese script3 . The closest siblingis the Javanese script which have rectangular form offont shape compared to round shape of Balinese script[14]. The registered Unicode is 1B00-1B7F.

4. Bataks
Batak tribe, mainly living in northern region of Sumateran Island (Sumatera Utara) in Indonesia, has been established for around 800-1000 years. Within that long
period, Batak people developed several subtribes andclans. The largest one (in population number) is Toba
subtribe, followed (in no particular order) by Karo,
Simalungun, Pakpak-Dairi, Angkola-Mandailing, andNias (Niha) people. Batak tribe has its own writing system which existed since 13th century AD. Batak people
themselves call their writing system Surat Batak (Surat= letters/writings) [15] shown in Fig. 1(d). Currently, it
is already included in Unicode (1BC0-1BFF).

In this preliminary work, to simplify the task we only use theromanized version of those ethnic scripts.

(a)

3. EXISTING INDONESIAN LARGE VOCABULARYDATA RESOURCES
The Indonesian speech corpora were developed by the R&D
Division of PT. Telekomunikasi Indonesia (R&D TELKOM)
in collaboration with ATR as a continuation of the APT (Asia
Pacific Telecommunity) project [16, 17]. Two types of Indonesian data resources available in both text and speechforms were used here: daily news task and telephone application task. They are described below.

3.1. Daily News Task
A raw text source for the daily news task has already been
generated by an Indonesian student [18]. The source wascompiled from “KOMPAS” and “TEMPO,” which are currently the biggest and most widely read Indonesian newspaper and magazine, respectively. This source consists of morethan 3160 articles, with around 600,000 sentences. R&D
TELKOM further processed the raw text source to generatea clean text corpus.
From this text data, we then selected phonetically-balanced
sentences by using the greedy search algorithm [19]; this produced a total of 3168 sentences. Then, clean and telephone
speech utterances were recorded, simultaneously, at sampling
frequencies of 16 and 8 kHz, respectively, by R&D TELKOM
in Bandung, Java Island, Indonesia. There were a total of 400
speakers (200 males and 200 females). Four original accents were covered: standard Indonesian, Bataks, Javanese,and Sundanese, denoted as “IND”, IND-BTK”,“IND-JAW”,
and “IND-SND”, respectively. However, all speakers wererequested to utter 110 sentences without any accents. In total, there were 44,000 speech utterances, which amounted toaround 43.35 hours of speech.

(b)3.2. Telephone Application Task

(c)

(d)

Fig. 1. Script of ethnic languages: (a) aksara Hanacaraka, (b)aksara Sunda, (c) Balinese script, and (d) surat Bataks.

3 http://en.wikipedia.org/wiki/Balinese alphabet

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With total of 2500 sentences from the telephone application domain were generated by R&D TELKOM. They were
derived from some of the necessary dialogs used in telephone services, including tele-home security, billing information services, reservation services, status tracking of
e-Government services and hearing impaired telecommunication services (HITS).
Using the same recording set-up as for the news task corpus, the speech utterances of 2500 sentences of telephone application task were recorded by R&D TELKOM in Bandung,Indonesia. The total number of speakers and the appropriate distribution of age and accents were also the same. Each
speaker uttered 100 sentences, resulting in a total of 40,000
utterances (36.15 hours of speech).

SLTU-2014, St. Petersburg, Russia, 14-16 May 2014
4. DESIGN AND COLLECTION OF INDONESIANETHNIC LANGUAGES

on grapheme transcription. This produced a total of 225
sentences for each language as shown in Table 2.

4.1. Raw Text Sources Collection
4.4. Parallel Sentences
Raw text sources are collected from online newspaper andmagazine: Penjabar-Semangat4 for Javanese, Sunda-News5
for Sundanese, Bali-Post6 for Balinese, and Halo-Moantondang7
for Bataks. Table 4.1 shows the total number of articles andsentences which have successfully been collected.
Table 1. Raw Text Corpora of Javanese, Sundanese, Balineseand Bataks.
Languages
Javanese
Sundanese
Balinese
Bataks# Articles
1583
1693
3919
1096

# Sentences
43336
39770
20436
36204

In addition to graphemically balanced sentences, we also
created fifty sentences of Indonesian language based on the
ATR basic travel expression corpus (BTEC) which has served
as the primary source for developing broad coverage speechtranslation systems [20]. Those sentences were then translated into Javanese, Sundanese, Balinese, and Bataks languages by native speakers. Table 4.1 shows a translated
sentence example of ”Apakah anda bersedia untuk makan
dengan saya besok malam?” (meaning ”Would you like tohave dinner with me tomorrow night?”).
4.5. Speech Corpora Collection

4.2. Pre-Processing and Validation
The initial forms of these documents contain numbers, punctuation, abbreviations, acronyms, names, and foreign words.
We then further processed the raw text sources to generateclean text corpora by:• converting all upper case letters into lower case• removing punctuation• changing numbers into words• select short sentences (max. 15 words/sentence)
resulting 6616 sentences of Javanese, 5717 sentences of Sundanese, 3249 Sentences of Balinese, and 6870 sentences of
Bataks, respectively.
After that, we selected 1000 sentences from cleaned text
corpora of each language to be validated by the native speakers. The validation is done in order to correct any errors in thesentences, as well as remove inappropriate sentences, resulting 823 sentences of Javanese, 954 sentences of Sundanese,
956 Sentences of Balinese, and 910 sentences of Bataks, respectively.
4.3. Graphemically-balanced Sentences
Given the validated text corpora, we then selected balanced
sentences by using the greedy search algorithm [19]; However, as the phonetically transcription of these ethnic languages are unknown, we selected balanced sentences based
4 www.penjebarsemangat.co.id
5 sundanews.com
6 www.balipost.co.id
7 halomoantondang.wordpress.com

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For speech recording, 40 native speakers were participated.
Ten native speakers (5 males and 5 females) of each Javanese, Sundanese, Balinese, Bataks language, which originally came from ethnics of Java, Sunda, Bali, and North
Sumatra. Each speaker was asked to utter 325 sentences, including 225 graphemically balanced sentences and 50 parallel
sentences (50 Indonesian sentences and 50 ethnic language
sentences). All speakers were requested to keep their accenteven during uttering Indonesian sentences. The Indonesianutterances with Javanese, Sundanese, Balinese, Bataks accents are denoted as “ACC-JAW”, “ACC-SND”, “IND-BLI”,
and “IND-BTK”, respectively, while the utterances of Javanese, Sundanese, Balinese, Bataks languages are denoted
as “JAW”, “SND”, “BLI”, and “BTK”. The speech recording
was conducted in a sound proof room in Jakarta, Indonesia.
Speech was recorded into WAV file at a 48 kHz sampling
rate with 16 bits resolution. The sampling rate was later
down-sampled to 16 kHz for further experiments.

5. DEVELOPMENT OF GRAPHEME-BASEDSPEECH RECOGNITION SYSTEM
The large vocabulary speech recognition of standard Indonesian language (denoted as “IND”) was developed using bothdaily news and telephone application tasks, while the speech
recognition of Indonesian ethnic languages were developed
using their specific ethnic language resources, including Javanese, Sundanese, Balinese, Bataks languages (denoted as“JAW”, “SND”, “BLI”, and “BTK”). As the phoneme set of
ethnic languages are unknown, all models are develop basedon grapheme unit. We also build multilingual acoustic modelin which we pooled all data together and build one acoustic
model (denoted as “MLT”). The parameter set-up, acoustic
modeling, language modeling, pronunciation dictionary andrecognition accuracy are described more fully below.

SLTU-2014, St. Petersburg, Russia, 14-16 May 2014

Table 2. Number of units and coverage rate of the training data resulting from the greedy search algorithm.
Graphame
Mono-grapheme
Left Bi-grapheme
Right Bi-grapheme
Tri-grapheme

Javanese# Units Coverage
27
100%
487
86.50%
482
86.07%
3269
53.99%

Sundanese# Units Coverage
27
100%
489
87.79%
487
87.59%
3197
52.56%

Balinese# Units Coverage
28
100%
441
82.28%
438
82.18%
2796
53.35%

Bataks# Units Coverage
23
100%
287
90.25%
285
90.19%
1767
71.42%

Table 3. A translated sentence example of ”Apakah anda bersedia untuk makan dengan saya besok malam?” (meaning ”Would
you like to have dinner with me tomorrow night?”)
Languages
IndonesianJavanese
Sundanese
Balinese
BataksSentences
Apakah anda bersedia untuk makan dengan saya besok malam?
Opo kowe gelem mangan bareng aku sesuk bengi?
Dupi anjeun sanggem kanggo tuang sareng abdi enjing wengi?
Napikeh mresidayang ragane buin mani peteng ngajeng sareng tiang?
Boha molo rap marjobut hita masogot bot ari?

5.1. Front-End Processing
We trained the ASR systems based on weighted finite statetransducers (WFSTs) [21] using the Kaldi toolkit [22]. The
frontend provides features every 10ms with 25ms width. For
each utterances in the speech training data, 13 static melfrequency cepstral coefficients (MFCC) including zeroth order for each frame are extracted and normalized with cepstrum mean normalization.
To incorporate the temporal structures and dependencies,
9 adjacent (center, 4 left, and 4 right) frames of MFCCs arestacked into one single feature vector leading to 117 dimensional super vectors (9x13 dimensions). It then reduced toan optimum 40 dimensions by applying a linear discriminant
analysis (LDA) and maximum likelihood linear transformation (MLLT)[23].
5.2. Model Training
All models are grapheme-based HMM with a standard threestate left-to-right HMM topology without skip states. Eachgrapheme is classified by its position in word (4 classes:
begin, end, internal and singleton). The context-dependent
cross-word trigraph HMMs was trained with GMM output
probability. This models totally include 15K Gaussians.
The dictionary of standard Indonesian, which is owned
R&D TELKOM, was derived from the daily news and telephone application text corpus. It consists of about 40K words
in total, including 30K original Indonesian words plus 8K
people and place names and 2K foreign words. All pronunciation of these words were manually developed by Indonesian
linguists. As for Javanese, Sundanese, Balinese and Bataks
ethnic languages, the dictionary was derived from the selected
text covering only about 2K words.

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Using the SRILM toolkit [24], we built n-gram language
models with modified Kneser-Ney smoothing [25] from eachof the text corpora (standard Indonesian and ethnic languages). The resulting language model contains 19K trigramsof Indonesian, about 4K trigrams of each ethic language.
5.3. Recognition Accuracy
In this experiments, we investigated the performance of ASR
systems on following test sets:• Standard Indonesian test set (“IND”,“IND-JAW”,“ACC-SND”, and “IND-BTK”) in which the speakers utter the Indonesian sentences without any accent.
• Accented Indonesian test set (“ACC-JAW”, “ACCSND”, “IND-BLI”, and “IND-BTK”) in which the
speakers utter the Indonesian sentences with heavy
ethnic accent.
• Ethnic test set (“JAW”, “SND”, “BLI”, and “BTK”) in
which the speakers utter the sentences of ethnic languages.
First, we investigated the performance of ASR using only
standard Indonesian acoustic model on various test sets: standard Indonesian, accented Indonesian and ethnic Indonesian
test sets as shown in Fig. 2. The pronunciation dictionary andlanguage model were set-up based on the corresponding test
set. The performance of ASR using only Indonesian acoustic
model on standard Indonesian test set could achieve less than
1% WER as expected. But then, the performance degradeson speech with heavy accents. Furthermore, it drops significantly up to more than 80% WER on ethnic test sets.

SLTU-2014, St. Petersburg, Russia, 14-16 May 2014

Fig. 2. The performance of standard Indonesian ASR on various test sets: standard Indonesian, accented Indonesian andethnic Indonesian test sets.

Fig. 4. The performance of various ASR (IND, JAW, SND,
BTK, BLI, MLT) on accented Indonesian test set.

Fig. 5. The performance of various ASR (IND, JAW, SND,
BTK, BLI, MLT) on ethnic test set.
Fig. 3. The performance of various ASR (IND, JAW, SND,
BTK, BLI, MLT) on standard Indonesian test set.

Next, Fig. 3, 4, and 5 show the comparison performance
between ASR systems that use standard Indonesian acoustic model (“IND”), ethnic-language-specific acoustic model(“JAW”, “SND”, “BTK”, “BLI”) and multilingual acoustic
model (“MLT”), which were tested on standard Indonesian,accented Indonesian and ethnic Indonesian test sets, respectively. On standard Indonesian test set, the ASR with Indonesian acoustic model perform the best, while on accented Indonesian test set, the ASR with multilingual acoustic model
perform the best. These results reveal that combining ethnic
speech data could improve the performance on heavily accented of Indonesian speech. However, the performance ofon ethnic test set is still very low, which is beyond 80% of
WER. In this case, the ASR systems with ethnic-languagespecific acoustic model still provide a better performance.
The best performances on ethnic test set were still achievedby ethnic-language-matched acoustic model. This may indicate that the acoustic characteristics of Indonesian are quite

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different from the acoustic characteristic of those ethnic languages. Therefore, finding optimum way to combine various
different acoustic speech may be necessary.

6. CONCLUSION
We have presented the collection of Indonesian ethnic speech
corpus, which includes Javanese, Sundanese, Balinese, andBataks spoken languages. This includes 225 graphemicallybalanced sentences and 50 parallel sentences. We have alsopresented the current state of ASR system development forIndonesian ethnic languages. The results reveal that the ASR
system with multilingual acoustic model could improve theperformance on heavily accented of Indonesian speech, but
not on ethnic language speech. This may indicate that the
acoustic characteristics of Indonesian are quite different fromthe acoustic characteristic of those ethnic languages. The
best performances on ethnic test set were still achieved by
ethnic-language-matched acoustic model. In the future, we

SLTU-2014, St. Petersburg, Russia, 14-16 May 2014
will investigate other techniques to combine various different speech acoustic of those ethnic languages, such the use ofmultilingual multilayer perceptron.

[13] M. Everson,“Preliminary proposal for encoding
additional Sundanese characters for old sundanesein the UCS,” http://www.unicode.org/L2/L2009/09190n3648-sundanese.pdf, 2009.

Acknowledgment[14] I.B.A. Sudewa,“Contemporary use of the Balinese script,” http://www.unicode.org/L2/L2003/03118balinese.pdf, 2003.

Part of this work was supported by Grant-in-Aid for Young
Scientists (KAKENHI Wakate-B Grant Number 24700172).
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