Relevant bibliographies by topics / Indonesia speech recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Indonesia speech recognition'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Indonesia speech recognition"

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Nasri, Andi. "Konversi Suara Ucapan Bahasa Indonesia Ke Sistem Bahasa Isyarat Indonesia (Sibi)." Ainet : Jurnal Informatika 2, no. 2 (2020): 7–13. http://dx.doi.org/10.26618/ainet.v2i2.4025.

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Dengan semakin berkembangnya teknologi speech recognition, berbagai software yang bertujuan untuk memudahkan orang tunarungu dalam berkomunikasi dengan yang lainnya telah dikembangkan. Sistem tersebut menterjemahkan suara ucapan menjadi bahasa isyarat atau sebaliknya bahasa isyarat diterjemahkan ke suara ucapan. Sistem tersebut sudah dikembangkan dalam berbagai bahasa seperti bahasa Inggris, Arab, Spanyol, Meksiko, Indonesia dan lain-lain. Khusus untuk bahasa Indonesia mulai juga sudah yang mencoba melakukan penelitian untuk membuat system seperti tersebut. Namun system yang dibuat masih terbatas pada Automatic Speech Recognition (ASR) yang digunakan dimana mempunyai kosa-kata yang terbatas. Dalam penelitian ini bertujuan untuk mengembangkan sistem penterjemah suara ucapan bahasa Indonesia ke Sistem Bahasa Isyarat Indonesia (SIBI) dengan data korpus yang lebih besar dan meggunkanan continue speech recognition untuk meningkatkan akurasi system.Dari hasil pengujian system menunjukan diperoleh hasil akurasi sebesar rata-rata 90,50 % dan Word Error Rate (WER) 9,50%. Hasil akurasi lebih tinggi dibandingkan penelitian kedua 48,75% dan penelitan pertama 66,67%. Disamping itu system juga dapat mengenali kata yang diucapkan secara kontinyu atau pengucapan kalimat. Kemudian hasil pengujian kinerja system mencapai 0,83 detik untuk Speech to Text dan 8,25 detik untuk speech to sign.
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Sukmawati, Nur Endah, Adhy Satriyo, and Sutikno. "Comparison of Feature Extraction MFCC and LPC in Automatic Speech Recognition for Indonesian." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 292–98. https://doi.org/10.12928/TELKOMNIKA.v15i1.3605.

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Speech recognition can be defined as the process of converting voice signals into the ranks of the word, by applying a specific algorithm that is implemented in a computer program. The research of speech recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for speech recognition in Indonesian language. This is important because the method can produce a high accuracy for a particular language does not necessarily produce the same accuracy for other languages, considering every language has different characteristics. Thus this research hopefully can help further accelerate the use of automatic speech recognition for Indonesian language. There are two main processes in speech recognition, feature extraction and recognition. The method used for comparison feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden Markov Model (HMM). The test results showed that the MFCC method is better than LPC in Indonesian language speech recognition.
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William, Ezra, and Amalia Zahra. "Speech Recognition Dengan Whisper Dalam Bahasa Indonesia." Action Research Literate 9, no. 2 (2025): 386–97. https://doi.org/10.46799/arl.v9i2.2573.

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Perkembangan teknologi kecerdasan buatan telah mendorong kemajuan dalam pengenalan suara (speech recognition), terutama dalam mendukung komunikasi digital yang lebih efisien. Salah satu model terbaru yang banyak digunakan adalah Whisper, yang dikembangkan oleh OpenAI dengan kemampuan pengenalan suara multibahasa yang diklaim memiliki akurasi tinggi. Namun, tantangan utama dalam implementasi teknologi ini di Indonesia adalah keterbatasan sumber daya data dalam bahasa lokal serta variasi aksen yang signifikan. Oleh karena itu, penelitian ini dilakukan untuk mengevaluasi kinerja model Whisper dalam mengenali dan mentranskripsi suara berbahasa Indonesia. Penelitian ini bertujuan untuk menganalisis tingkat akurasi Whisper dalam pengenalan ucapan bahasa Indonesia berdasarkan Word Error Rate (WER) serta membandingkannya dengan model XLS-R dan XLSR-53. Metode yang digunakan dalam penelitian ini adalah pendekatan komparatif dengan melakukan fine-tuning terhadap model Whisper menggunakan dataset Common Voice 13 dalam bahasa Indonesia. Evaluasi model dilakukan dengan mengukur WER pada tahap pelatihan dan pengujian. Hasil penelitian menunjukkan bahwa model Whisper memiliki performa terbaik dibandingkan model XLS-R dan XLSR-53 dalam mengenali ucapan bahasa Indonesia. Nilai WER Training yang diperoleh adalah 22.33505%, sedangkan nilai WER Testing adalah 19.774909%. Hal ini menunjukkan bahwa model Whisper lebih unggul dalam menangani variasi aksen dan kondisi akustik dibandingkan dengan model lainnya. Keunggulan ini terutama disebabkan oleh pelatihan berbasis data yang lebih besar serta kemampuan adaptasi model terhadap berbagai bahasa. Implikasi penelitian ini memberikan kontribusi dalam pengembangan teknologi speech recognition berbahasa Indonesia serta meningkatkan aksesibilitas bagi pengguna dalam berbagai sektor, seperti pendidikan, layanan publik, dan teknologi komunikasi.
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Novela, Martin, and T. Basaruddin. "Dataset Suara dan Teks Berbahasa Indonesia Pada Rekaman Podcast dan Talk show." JURNAL FASILKOM 11, no. 2 (2021): 61–66. http://dx.doi.org/10.37859/jf.v11i2.2628.

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Salah satu faktor keberhasilan suatu model pembelajaran dalam machine learning atau deep learning adalah dataset yang digunakan. Pada tulisan ini menyajikan dataset suara dari rekaman podcast dan talk show beserta transkripsi berbahasa Indonesia. Dataset ini disajikan karena belum adanya ketersediaan dataset berbahasa Indonesia yang dapat diakses secara publik untuk digunakan pada pembelajaran model Text-to-Speech ataupun Audio Speech Recognition. Dataset terdiri dari 3270 rekaman yang diproses untuk mendapatkan transkripsi berupa teks atau kalimat berbahasa Indonesia. Dalam pembuatan dataset ini dilakukan beberapa tahapan seperti pra-pemrosesan, tahapan translasi, tahapan validasi pertama dan tahapan validasi kedua. Dataset dibuat dengan format yang mengikuti format dari dataset LJSpeech untuk memudahkan pemrosesan dataset ketika digunakan dalam suatu model sebagai input. Dataset ini diharapkan dapat membantu meningkatkan kualitas pembelajaran untuk pemrosesan Text-to-Speech seperti pada model Tacotron2 ataupun pada pemrosesan Audio Speech Recognition untuk bahasa Indonesia.
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Suhardiyanto, Totok. "Mengapa Mesin Pencari Suara Gagal Mengenali Bahasa Indonesia? Sebuah Kajian Awal Tentang Asr (automatic speech recognition) Bahasa Indonesia." JURNAL ARBITRER 1, no. 1 (2013): 88. http://dx.doi.org/10.25077/ar.1.1.88-98.2013.

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This paper is about the study of Indonesian Automatic Speech Recognition (ASR) designed by Informational- Technological Computer ( TIK).Specifically, this paper is aimed at describing how this tool operates in recognizing some in-puts in Indonesian language. TIK industry has something to do with Indonesian PWO where the use of the smart phones developes massively in Indonesia. This study processes around 10.774 data in Indonesian language in form of sentence, phrase, and word. From this number, less than 20% can be categorized perfect. The others have an error in format and recognition. This is due to some factors bringing about the failure of ASR in recognising the in put in Indonesian language.
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,, Muhammad, Syahroni Hidayat, and Ahmad Zuli Amrullah. "Speech Recognition Untuk Aplikasi Kamus Bahasa Indonesia-Sumbawa Berbasis Android." Jurnal Bumigora Information Technology (BITe) 1, no. 2 (2019): 126–37. http://dx.doi.org/10.30812/bite.v1i2.606.

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ABSTRAK
 Sumbawa sebagai salah satu daerah yang dianugrahi potensi wisata yang beragam menjadikan daya tarik masyarakat luar Sumbawa (wisatawan) untuk berkunjung, bekerja, maupun untuk belajar. Namun terkadang bahasa menjadi salah satu kendala yang dihadapi mayarakat luar Sumbawa jika ingin berinteraksi dengan masyarakat asli Sumbawa. Sehingga dibutuhkan sebuah instrument yang bisa digunakan sehingga perbedaan bahasa tidak menjadi kendala dalam berinteraksi yaitu kamus. Oleh karena itu, kamus yang disajikan haruslah sesuai dengan teknologi yang banyak diminati oleh masyarakat Indonesia pada umumnya yaitu smartphone Android dikarenakan fitur-fitur yang tersedia dalam smartphone tersebut. Salah satu fiturnya adalah speech recognition.Perancangan sistem ini dilakukan dengan metodologi waterfall yang terdiri dari proses analisis, desain, pengkodean, pengujian, dan terakhir pemeliharaan. Tools yang digunakan adalah Android Studio dan DB Browser for SQLite (DB4S). Metode pengujian menggunakan Black Box untuk uji fungsionalitas aplikasi dan Word Correct Rate (WCR) untuk menguji akurasi sistem dengan menggunakan 30 kata yang berbeda dan setiap kata diulang sebanyak 10 kali.Hasil yang sudah dicapai dalam penelitian ini adalah terciptanya aplikasi Kamus Bahasa Indonesia- Sumbawa Berbasis Android dengan memanfaatkan teknologi speech recognition.Kesimpulan dari penelitian ini adalah Uji fungsionalitas menunjukkan fitur-fitur aplikasi dapat bekerja dengan baik ketika offline maupun online. Sedangkan untuk uji coba akurasi sistem didapatkan hasil WCR secara berturut-turut sebesar 92.67% ketika offline dan 95.33% ketika online.
 ABSTRACT
 Sumbawa as one of the areas that is blessed with diverse tourism potential makes the appeal of people outside Sumbawa (tourists) to visit, work, or to study. But sometimes language becomes one of the obstacles faced by people outside Sumbawa if they want to interact with the native people of Sumbawa. So we need an instrument that can be used so that differences in language do not become obstacles in interacting with the dictionary. Therefore, the dictionary presented must be in accordance with the technology that is in great demand by the Indonesian people in general, namely Android smartphones because of the features available in these smartphones. One of the features is speech recognition. The design of this system is done by the waterfall methodology which consists of the process of analysis, design, coding, testing, and finally maintenance. The tools used are Android Studio and DB Browser for SQLite (DB4S). The testing method uses Black Box to test application functionality and Word Correct Rate (WCR) to test the accuracy of the system using 30 different words and each word is repeated 10 times. The results achieved in this study are the creation of an Indonesian-Sumbawa-based Dictionary application Android by utilizing speech recognition technology. The conclusion of this research is the functionality test shows that the application features can work well when offline or online. Whereas for testing the accuracy of the system the WCR results obtained were 92.67% when offline and 95.33% when online.
 
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Nurhasanah, Youllia Indrawaty, Irma Amelia Dewi, and Bagus Ade Saputro. "Iqro Reading Learning System through Speech Recognition Using Mel Frequency Cepstral Coefficient (MFCC) and Vector Quantization (VQ) Method." IJAIT (International Journal of Applied Information Technology) 2, no. 01 (2018): 29. http://dx.doi.org/10.25124/ijait.v2i01.1173.

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Historically, the study of Qur'an in Indonesia evolved along with the spread of Islam. Learning methods of reading the Qur'an have been found ranging from al-Baghdadi, al-Barqi, Qiraati, Iqro', Human, Tartila, and others, which can make it easier to learn to read the Qur'an. Currently, the development of speech recognition technology can be used for the detection of Iqro vol 3 reading pronunciations. Speech recognition consists of two general stages of feature extraction and speech matching. The feature extraction step is used to derive speech-feature and speech-matching stages to compare compatibility between test sound and train voice. The speech recognition method used to recognize Iqro readings is extracting speech signal features using Mel Frequency Cepstral Coefficient (MFCC) and classifying them using Vector Quantization (VQ) to get the appropriate speech results. The result of testing for speech recognition system of Iqro reading has been tested for 30 peoples as a sample of data and there are 6 utterances indicating the information failed, so the system has a success rate of 80%.
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Henry, Henry, and Eryc Eryc. "Speech Recognition Untuk Membantu Pelafalan Hanyu Pinyin Sebagai Bagian Dari Edukasi Bahasa Mandarin." Jurnal Ilmiah Edutic : Pendidikan dan Informatika 10, no. 2 (2024): 117–28. http://dx.doi.org/10.21107/edutic.v10i2.22633.

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Pada era globalisasi, kemampuan berbahasa internasional semakin penting dalam bidang studi ataupun bidang bisnis. Salah satu bahasa internasional yang sering dipakai adalah bahasa Mandarin. Beberapa negara sudah mulai memasukkan bidang studi bahasa Mandarin dalam kurikulumnya, salah satunya adalah Indonesia. Perbedaan karakteristik bahasa Indonesia dan Mandarin membuat pelajar bahasa Mandarin di Indonesia cenderung melakukan kesalahan pelafalan pada bahasa Mandarin. Speech Recognition adalah cabang dari artificial intelligence yang memungkinkan komputer untuk menerima input berupa suara. Speech recognition dapat digunakan untuk merancang aplikasi yang mampu melatih kemampuan pelafalan bahasa Mandarin. Penelitian ini bertujuan untuk meneliti proses perancangan dan evaluasi aplikasi berbasis mobile yang menggunakan speech recognition untuk membantu pelajar bahasa Mandarin dengan pelatihan pelafalan hanyu pinyin. Aplikasi ini dirancang menggunakan metode SDLC tipe waterfall dan menggunakan bahasa pemograman dart dengan framework flutter dan mengguakan package speech_to_text dan flutter_tts. Pengujian aplikasi menggunakan pendekatan black-box testing. Pengumpulan data secara kuantitatif dilakukan dengan penyebaran kuesioner kepada 33 pengguna aplikasi yang dirancang dan hasil kuesioner menunjukkan hasil “Sangat Efektif” dari segi pengoperasian, tampilan, dan isi materi aplikasi menggunakan penilaian skala interval likert.
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Amrullah, Ahmad Zuli, and Khurniawan Eko Saputro. "Analisis dan Perancangan Kamus Interaktif Bahasa Isyarat Indonesia dengan Speech Recognition." Jurnal Bumigora Information Technology (BITe) 1, no. 2 (2019): 110–15. http://dx.doi.org/10.30812/bite.v1i2.604.

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ABSTRAK
 
 
 Intisari – Menurut data Survei Sosial Ekonomi Nasional (Susenas) pada tahun 2012 terdapat sekitar 9,9 juta anak Indonesia menyandang disabilitas. Sekitar 7.87% dari total jumlah penyandang disabilitas tersebut mengalami tunarungu atau keterbatasan mendengar. Penyandang tunarungu melakukan komunikasi dengan menggunakan Bahasa isyarat. Karena tidak semua orang mengerti dengan bahasa isyarat maka dibutuhkan alat bantu atau aplikasi untuk berkomunikasi dengan penyandang tunarungu. Keterbatasan dalam berkomunikasi antara orang biasa dengan penyandang tunarungu. Oleh karena ity, untuk membantu mahasiswa dan dosen berkomunikasi dengan mahasiswa yang tunarung maka dibutuhkan aplikasi kamus Bahasa isyarat dengan Speech Recognition. Pengembangan aplikasi ini menggunakan metode pengembangan aplikasi waterfall. Dimana setiap alur berjalan secara selaras dan memudahkan untuk mencari kesalahan system. Pengujian dilakukan dengan verifikasi kebutuhan untuk memastikan produk perangkat lunak yang dihasilkan sesuai dengan spesifikasi yang ditentukan.
 Kata Kunci: Bahasa isyarat; kamus; speech recognition;
 ABSTRACT
 
 Digest - According to data from the National Socio-Economic Survey (Susenas) in 2012 there were around 9.9 million Indonesian children with disabilities. Around 7.87% of the total number of persons with disabilities experience hearing impairment or hearing impairment. People with hearing impairment communicate using sign language. Because not everyone understands sign language, tools or applications are needed to communicate with deaf people. Limitations in communicating between ordinary people and hearing impaired people. Therefore, to help students and lecturers communicate with students who are fussy, it requires a sign language dictionary application with Speech Recognition. This application development uses the waterfall application development method. Where each flow runs in harmony and makes it easy to find system errors. The test is carried out by verifying the need to ensure that the software product is produced according to the specified specifications.
 
 Keywords: Signal language; dictionary; speech recognition;
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Hilman, F. Pardede, Adhi Purwoko, Zilvan Vicky, Ramdan Ade, and Krisnandi Dikdik. "Deep convolutional neural networks-based features for Indonesian large vocabulary speech recognition." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 610–17. https://doi.org/10.11591/ijai.v12.i2.pp610-617.

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There are great interests in developing speech recognition using deep learning technologies due to their capability to model the complexity of pronunciations, syntax, and language rules of speech data better than the traditional hidden Markov model (HMM) do. But, the availability of large amount of data is necessary for deep learning-based speech recognition to be effective. While this is not a problem for mainstream languages such as English or Chinese, this is not the case for non-mainstream languages such as Indonesian. To overcome this limitation, we present deep features based on convolutional neural networks (CNN) for Indonesian large vocabulary continuous speech recognition in this paper. The CNN is trained discriminatively which is different from usual deep learning implementations where the networks are trained generatively. Our evaluations show that the proposed method on Indonesian speech data achieves 7.26% and 9.01% error reduction rates over the state-of-the-art deep belief networks-deep neural networks (DBN-DNN) for large vocabulary continuous speech recognition (LVCSR), with Mel frequency cepstral coefficients (MFCC) and filterbank (FBANK) used as features, respectively. An error reduction rate of 6.13% is achieved compared to CNN-DNN with generative training.
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Dissertations / Theses on the topic "Indonesia speech recognition"

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Martin, Terrence Lance. "Towards improved speech recognition for resource poor languages." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/35771/1/Terrence_Martin_Thesis.pdf.

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In recent times, the improved levels of accuracy obtained by Automatic Speech Recognition (ASR) technology has made it viable for use in a number of commercial products. Unfortunately, these types of applications are limited to only a few of the world’s languages, primarily because ASR development is reliant on the availability of large amounts of language specific resources. This motivates the need for techniques which reduce this language-specific, resource dependency. Ideally, these approaches should generalise across languages, thereby providing scope for rapid creation of ASR capabilities for resource poor languages. Cross Lingual ASR emerges as a means for addressing this need. Underpinning this approach is the observation that sound production is largely influenced by the physiological construction of the vocal tract, and accordingly, is human, and not language specific. As a result, a common inventory of sounds exists across languages; a property which is exploitable, as sounds from a resource poor, target language can be recognised using models trained on resource rich, source languages. One of the initial impediments to the commercial uptake of ASR technology was its fragility in more challenging environments, such as conversational telephone speech. Subsequent improvements in these environments has gained consumer confidence. Pragmatically, if cross lingual techniques are to considered a viable alternative when resources are limited, they need to perform under the same types of conditions. Accordingly, this thesis evaluates cross lingual techniques using two speech environments; clean read speech and conversational telephone speech. Languages used in evaluations are German, Mandarin, Japanese and Spanish. Results highlight that previously proposed approaches provide respectable results for simpler environments such as read speech, but degrade significantly when in the more taxing conversational environment. Two separate approaches for addressing this degradation are proposed. The first is based on deriving better target language lexical representation, in terms of the source language model set. The second, and ultimately more successful approach, focuses on improving the classification accuracy of context-dependent (CD) models, by catering for the adverse influence of languages specific phonotactic properties. Whilst the primary research goal in this thesis is directed towards improving cross lingual techniques, the catalyst for investigating its use was based on expressed interest from several organisations for an Indonesian ASR capability. In Indonesia alone, there are over 200 million speakers of some Malay variant, provides further impetus and commercial justification for speech related research on this language. Unfortunately, at the beginning of the candidature, limited research had been conducted on the Indonesian language in the field of speech science, and virtually no resources existed. This thesis details the investigative and development work dedicated towards obtaining an ASR system with a 10000 word recognition vocabulary for the Indonesian language.
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Book chapters on the topic "Indonesia speech recognition"

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Budi, Indra, Stéphane Bressan, Gatot Wahyudi, Zainal A. Hasibuan, and Bobby A. A. Nazief. "Named Entity Recognition for the Indonesian Language: Combining Contextual, Morphological and Part-of-Speech Features into a Knowledge Engineering Approach." In Discovery Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11563983_7.

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Ayu Purwarianti, Dessi Puji Lestari, and Devin Hoesen. "Kecerdasan Artifisial untuk Pengolahan Ucapan dan Teks Berbahasa Indonesia." In Prosiding Use Cases Artificial Intelligence Indonesia: Embracing Collaboration for Research and Industrial Innovation in Artificial Intelligence. Penerbit BRIN, 2023. http://dx.doi.org/10.55981/brin.668.c536.

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Teknologi Pemrosesan Bahasa Alami atau Natural Language Processing (NLP), baik pada teks maupun suara untuk bahasa Indonesia, sudah mengalami perkembangan yang pesat. Teknologi NLP dapat membantu pelaksanaan berbagai proses dalam sebuah institusi supaya lebih efisien. Dalam kesempatan ini, akan dibahas tiga use case pemanfaatan teknologi NLP di institusi, yaitu regulatory technology, meeting transcription, dan voice biometrics. Regulatory technology adalah sebuah teknologi berbasis NLP yang bertujuan membantu pengecekan peraturan ataupun dokumen perusahaan secara otomatis. Regulatory technology akan membandingkan peraturan atau dokumen perusahaan dengan berbagai peraturan pemerintah terkait yang dikumpulkan secara otomatis dari berbagai situs peraturan pemerintah. Regulatory technology memanfaatkan berbagai teknologi NLP untuk pemrosesan teks, baik pencarian, ekstraksi, maupun klasifikasi teks. Saat ini, produk regulatory technology versi 1 sudah dipasang di salah satu bank di Indonesia dan sedang dikustomisasi untuk pemanfaatan di salah satu badan legislatif di indonesia. Meeting transcription adalah teknologi berbasis pemrosesan suara yang bertujuan mengubah suara hasil rekaman rapat ke dalam teks transkripsinya. Hasil transkripsi rekaman suara rapat mencakup informasi teks yang diucapkan oleh setiap peserta rapat dan ID peserta rapat tersebut. Meeting transcription menggunakan teknologi automatic speech recognition (ASR) untuk mengubah suara menjadi teks; teknologi Speech Diarization untuk membedakan suara peserta rapat, ekstraksi kata kunci, dan pencarian catatan rapat maupun segmen percakapan. Dalam pengujian untuk 8 dataset, teknologi ASR bahasa Indonesia yang dikembangkan memiliki nilai akurasi yang lebih tinggi dibandingkan Google ASR Bahasa Indonesia. Teknologi meeting transcription ini sudah dipasang di lembaga pemerintah, BUMN, dan lembaga legislatif di Indonesia. Voice Biometrics merupakan salah satu alternatif teknologi autentikasi identitas dengan berdasar pada suara. Voice Biometrics dapat digunakan secara online melalui VoIP maupun telepon, juga secara offline atau tatap muka langsung. Saat ini, akurasi voice biometrics dengan suara bersih mencapai 99%, sedangkan dengan suara yang memiliki noise mencapai 92%. Saat ini, produk voice biometrics sudah dipasang di salah satu bank di Indonesia untuk layanan customer service melalui telepon.
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Rock, Michael T., and David P. Angel. "The Role of Environmental Regulatory Agencies in Sustainability: Korea and Indonesia." In Industrial Transformation in the Developing World. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780199270040.003.0012.

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The first step, but by no means the last, in the process of improving the environmental performance of manufacturing plants, firms, and industries in East Asia requires the building and strengthening of traditional environmental regulatory systems. Without this, policy integration is not likely to work and without effective policy integration, governments in East Asia are not likely to be able to link environmental intensities reduction policies to the technological capabilities policies of economic and industrial development agencies. Because most governments in East Asia pursued ‘grow first, clean up later’ environmental strategies and because traditional economic and industrial development agencies are so closely linked to their counterparts in private industry, many (Lee and So 1999; Lohani 1998; Smil 1997; Eder 1996; Bello and Rosenfeld 1992) are skeptical of the ability of governments in this region to build and sustain traditional environmental regulatory agencies. But there is growing evidence that numerous governments in this region, including in Singapore, Malaysia, and Indonesia in Southeast Asia and Taiwan Province of China, China, and Korea in Northeast Asia have made significant progress in building traditional command and control regulatory agencies (Rock 2002a; Aden et al. 1999). Everywhere in East Asia, this was and is a lengthy, costly, difficult, contentious, and time-consuming process. The speed and alacrity with which governments succeed depends on an intricate interplay of international pressures, the nature of domestic politics, the capacity and capabilities of the state, and the rapidity with which new ideas about the environment spread (Rock 2002a). Sometimes, as in Taiwan Province of China, international pressures associated with the loss of diplomatic recognition and citizen pressures associated with democratization have exerted powerful influences on the ruling party, the KMT, to build a strong and capable environmental regulatory agency as a way of demonstrating to the world and Taiwan’s citizens that the government was environmentally responsible (Rock 2002a). Other times, as in Singapore, a benevolent despot committed to creating a clean and green Singapore used the powers of government and a highly capable bureaucracy to create a credible environmental agency (Rock 2002a).
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Conference papers on the topic "Indonesia speech recognition"

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Santosa, Agung, Asril Jarin, Elvira Nurfadhilah, Mohammad Teduh Uliniansyah, Tri Sampurno, and Radhiyatul Fajri. "End-to-End Phoneme Recognition in Bahasa Indonesia with Pretrained Speech Embeddings and 1D-CNN Using CTC." In 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, 2024. http://dx.doi.org/10.1109/ic3ina64086.2024.10732294.

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Adila, Aulia, Dessi Lestari, Ayu Purwarianti, Dipta Tanaya, Kurniawati Azizah, and Sakriani Sakti. "Enhancing Indonesian Automatic Speech Recognition: Evaluating Multilingual Models with Diverse Speech Variabilities." In 2024 27th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA). IEEE, 2024. https://doi.org/10.1109/o-cocosda64382.2024.10800336.

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Fathoni, Ali Nur, Inaya Retno Putri, Hesti Khuzaimah Nurul Yusufiyah, Fendi Achmad, Nur Kholis, and Yuli Sutoto Nugroho. "Design and Implementation of an Indonesian Speech Recognition Application Based on Mel Frequency Cepstral Coefficients and K-Nearest Neighbor Classification." In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2024. https://doi.org/10.1109/isriti64779.2024.10963607.

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Idananta, Kanyadian, and Kristianus Oktriono. "Determining the voiceprint recognition on the basis of emotional speech signal: Indonesia language." In 2017 3rd International Conference on Information Management (ICIM). IEEE, 2017. http://dx.doi.org/10.1109/infoman.2017.7950414.

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Mawaddah, Nurul, Charlie Rolando Tamba, Donny Hutagalung, Frans Nainggolan, Eka Lubis, and Eko Jamzuri. "Automatic Speech Recognition for Human-Robot Interaction on The Humanoid Robot Barelang 7." In Proceedings of the 6th International Conference on Applied Engineering, ICAE 2023, 7 November 2023, Batam, Riau islands, Indonesia. EAI, 2024. http://dx.doi.org/10.4108/eai.7-11-2023.2342940.

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Muljono, Askarya Qaulan Syadida, De Rosal Ignatius Moses Setiadi, and Andik Setyono. "Sphinx4 for Indonesian continuous speech recognition system." In 2017 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2017. http://dx.doi.org/10.1109/isemantic.2017.8251881.

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Maulana, Muhammad Rizki Aulia Rahman, and Mohamad Ivan Fanany. "Indonesian audio-visual speech corpus for multimodal automatic speech recognition." In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2017. http://dx.doi.org/10.1109/icacsis.2017.8355062.

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Satriawan, Cil Hardianto, and Dessi Puji Lestari. "Feature-based noise robust speech recognition on an Indonesian language automatic speech recognition system." In 2014 International Conference on Electrical Engineering and Computer Science (ICEECS). IEEE, 2014. http://dx.doi.org/10.1109/iceecs.2014.7045217.

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Wisesty, Untari N., Adiwijaya, and Widi Astuti. "Feature extraction analysis on Indonesian speech recognition system." In 2015 3rd International Conference on Information and Communication Technology (ICoICT ). IEEE, 2015. http://dx.doi.org/10.1109/icoict.2015.7231396.

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Wunarso, Novita Belinda, and Yustinus Eko Soelistio. "Towards Indonesian speech-emotion automatic recognition (I-SpEAR)." In 2017 4th International Conference on New Media Studies (CONMEDIA). IEEE, 2017. http://dx.doi.org/10.1109/conmedia.2017.8266038.

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