Development of Indonesian Large Vocabulary Continuous Speech Recognition System within A-STAR Project

By Sakriani Watiasri Sakti, Eka Kelana, Hammam Riza, Shinsuke Sakai, K. Markov, Satoshi Nakamura. 2008

63 citations recorded by the source corpus.

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In Speech & Audio

Citations in the corpus

VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019 (2019, background): “The source corpora of the surprise language are describe here [21, 22], and further details can be found here [3].”
The Zero Resource Speech Challenge 2019: TTS without T (2019, background): “The Surprise language is Indonesian, for which the corpora are derived from those developed in [22, 23].”
Cyclic Spectral Modeling for Unsupervised Unit Discovery into Voice Conversion with Excitation and Waveform Modeling (2020, methodology): “The dataset of the 2019-task [10] in ZSC 2020 (1)consisted of English and Surprise [29, 30] languages, where each has a subset of voice dataset and unit dataset.”
Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020) (2020, methodology): “41 for the surprise language [27, 28], when the Maximum a Posteriori (MAP) classification is scored using the Levenshtein distance and contiguous classmate frames are merged (Table 2).”
Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling (2019, methodology): “English is used for development while the surprise language (Indonesian) [26, 27] is used for test only.”
Development of HMM-based Indonesian Speech Synthesis (2008, methodology): “In this study, this was automatically done by Viterbi alignment of the spoken utterances and the corresponding transcription using the Indonesian speech recognition system [18].”
Transformer VQ-VAE for Unsupervised Unit Discovery and Speech Synthesis: ZeroSpeech 2020 Challenge (2020, background): “The source corpora for the surprise language are described here [20, 21], and further details can be found [1].”
Unsupervised Acoustic Unit Representation Learning for Voice Conversion using WaveNet Auto-encoders (2020, background): “The 2019 part of the ZeroSpeech 2020 challenge corpus contains two languages: English dataset for development and a surprise language [33, 34] dataset for test.”
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge (2020, background): “Indonesian is a low-resource Austronesian language widely used as a lingua franca [36, 37].”
Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion (2019, methodology): “In the ZeroSpeech 2019 Challenge [19], we achieve 2 nd place in terms of low bitrate under a strong di-mension constraint on the Surprise Language [20][21] leader-board.”
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units (2020, methodology): “The development data and surprise data are the same as in [2] (see [17, 18]).”