Dessi Puji Lestari

Also published as: Dessi Lestari


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Indonesian-English Code-Switching Speech Recognition Using the Machine Speech Chain Based Semi-Supervised Learning
Rais Vaza Man Tazakka | Dessi Lestari | Ayu Purwarianti | Dipta Tanaya | Kurniawati Azizah | Sakriani Sakti
Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024

Indonesia is home to a diverse linguistic landscape, where individuals seamlessly transition between Indonesian, English, and local dialects in their everyday conversations—a phenomenon known as code-switching. Understanding and accommodating this linguistic fluidity is essential, particularly in the development of accurate speech recognition systems. However, tackling code-switching in Indonesian poses a challenge due to the scarcity of paired code-switching data. Thus, this study endeavors to address Indonesian-English code-switching in speech recognition, leveraging unlabeled data and employing a semi-supervised technique known as the machine speech chain. Our findings demonstrate that the machine speech chain method effectively enhances Automatic Speech Recognition (ASR) performance in recognizing code-switching between Indonesian and English, utilizing previously untapped resources of unlabeled data.


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Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian
Ruhiyah Widiaputri | Ayu Purwarianti | Dessi Lestari | Kurniawati Azizah | Dipta Tanaya | Sakriani Sakti
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Despite being the world’s fourth-most populous country, the development of spoken language technologies in Indonesia still needs improvement. Most automatic speech recognition (ASR) systems that have been developed are still limited to transcribing the exact word-by-word, which, in many cases, consists of ambiguous sentences. In fact, speakers use prosodic characteristics of speech to convey different interpretations, which, unfortunately, these systems often ignore. In this study, we attempt to resolve structurally ambiguous utterances into unambiguous texts in Indonesian using prosodic information. To the best of our knowledge, this might be the first study to address this problem in the ASR context. Our contributions include (1) collecting the Indonesian speech corpus on structurally ambiguous sentences; (2) conducting a survey on how people disambiguate structurally ambiguous sentences presented in both text and speech forms; and (3) constructing an Indonesian ASR and meaning interpretation system by utilizing both cascade and direct approaches to map speech to text, along with two additional prosodic information signals (pause and pitch). The experimental results reveal that it is possible to disambiguate these utterances. In this study, the proposed cascade system, utilizing Mel-spectrograms concatenated with F0 and energy as input, achieved a disambiguation accuracy of 79.6%, while the proposed direct system with the same input yielded an even more impressive disambiguation accuracy of 82.2%.


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Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas
Sashi Novitasari | Quoc Truong Do | Sakriani Sakti | Dessi Lestari | Satoshi Nakamura
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Identifying Deception in Indonesian Transcribed Interviews through Lexical-based Approach
Tifani Warnita | Dessi Puji Lestari
Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation

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Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation
Candy Olivia Mawalim | Dessi Puji Lestari | Ayu Purwarianti
Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation