--- dataset_info: - config_name: age_cv21_en_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 186183226 num_examples: 1000 download_size: 167683505 dataset_size: 186183226 - config_name: age_cv21_ta_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 170567186 num_examples: 1000 download_size: 158065094 dataset_size: 170567186 - config_name: age_cv21_th_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 113020062 num_examples: 775 download_size: 101628338 dataset_size: 113020062 - config_name: age_cv21_vi_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 96975286 num_examples: 833 download_size: 88580998 dataset_size: 96975286 - config_name: age_cv21_zh_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 188659644 num_examples: 1000 download_size: 173300554 dataset_size: 188659644 - config_name: er_emota_ta_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 83507312 num_examples: 936 download_size: 80986133 dataset_size: 83507312 - config_name: er_esd_en_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 87158462 num_examples: 1000 download_size: 81742336 dataset_size: 87158462 - config_name: er_esd_zh_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 101894566 num_examples: 1000 download_size: 94654115 dataset_size: 101894566 - config_name: er_indowave_id_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 29823610 num_examples: 300 download_size: 27388508 dataset_size: 29823610 - config_name: er_m3ed_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 48528858 num_examples: 1000 download_size: 48458277 dataset_size: 48528858 - config_name: er_tec_ta_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 78326697 num_examples: 165 download_size: 74326171 dataset_size: 78326697 - config_name: er_thai_ser_th_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 185392818 num_examples: 955 download_size: 181829776 dataset_size: 185392818 - config_name: gr_cv21_en_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 187866020 num_examples: 1000 download_size: 169324799 dataset_size: 187866020 - config_name: gr_cv21_id_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 130792938 num_examples: 1000 download_size: 120466361 dataset_size: 130792938 - config_name: gr_cv21_ta_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 176204446 num_examples: 1000 download_size: 162459066 dataset_size: 176204446 - config_name: gr_cv21_th_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 108838190 num_examples: 747 download_size: 97776449 dataset_size: 108838190 - config_name: gr_cv21_vi_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 91260424 num_examples: 765 download_size: 83215420 dataset_size: 91260424 - config_name: gr_cv21_zh_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 190876448 num_examples: 1000 download_size: 174080501 dataset_size: 190876448 - config_name: gr_emota_ta_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 83506104 num_examples: 936 download_size: 80985600 dataset_size: 83506104 - config_name: gr_fleurs_en_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 204483500 num_examples: 647 download_size: 157338564 dataset_size: 204483500 - config_name: gr_fleurs_km_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 356936178 num_examples: 765 download_size: 322718184 dataset_size: 356936178 - config_name: gr_indowave_id_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 29822290 num_examples: 300 download_size: 27388224 dataset_size: 29822290 - config_name: gr_m3ed_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 47045258 num_examples: 1000 download_size: 46987421 dataset_size: 47045258 - config_name: gr_openslr_ta_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 192957552 num_examples: 1000 download_size: 167898810 dataset_size: 192957552 - config_name: gr_sfdusc_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 140402274 num_examples: 1000 download_size: 136741903 dataset_size: 140402274 - config_name: gr_sg_streets_utterance_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 81976388 num_examples: 492 download_size: 81169110 dataset_size: 81976388 - config_name: gr_smaldusc_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 244481562 num_examples: 1000 download_size: 226453857 dataset_size: 244481562 - config_name: gr_thai_elderly_th_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 166194232 num_examples: 992 download_size: 129420345 dataset_size: 166194232 - config_name: gr_thai_ser_th_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 185391218 num_examples: 955 download_size: 181801822 dataset_size: 185391218 - config_name: gr_vietnam_celeb_30 features: - name: context dtype: audio: sampling_rate: 16000 - name: instruction dtype: string - name: answer dtype: string - name: audio_length dtype: float64 - name: language dtype: string splits: - name: train num_bytes: 247174628 num_examples: 1000 download_size: 246693654 dataset_size: 247174628 configs: - config_name: age_cv21_en_30 data_files: - split: train path: age_cv21_en_30/train-* - config_name: age_cv21_ta_30 data_files: - split: train path: age_cv21_ta_30/train-* - config_name: age_cv21_th_30 data_files: - split: train path: age_cv21_th_30/train-* - config_name: age_cv21_vi_30 data_files: - split: train path: age_cv21_vi_30/train-* - config_name: age_cv21_zh_30 data_files: - split: train path: age_cv21_zh_30/train-* - config_name: er_emota_ta_30 data_files: - split: train path: er_emota_ta_30/train-* - config_name: er_esd_en_30 data_files: - split: train path: er_esd_en_30/train-* - config_name: er_esd_zh_30 data_files: - split: train path: er_esd_zh_30/train-* - config_name: er_indowave_id_30 data_files: - split: train path: er_indowave_id_30/train-* - config_name: er_m3ed_30 data_files: - split: train path: er_m3ed_30/train-* - config_name: er_tec_ta_30 data_files: - split: train path: er_tec_ta_30/train-* - config_name: er_thai_ser_th_30 data_files: - split: train path: er_thai_ser_th_30/train-* - config_name: gr_cv21_en_30 data_files: - split: train path: gr_cv21_en_30/train-* - config_name: gr_cv21_id_30 data_files: - split: train path: gr_cv21_id_30/train-* - config_name: gr_cv21_ta_30 data_files: - split: train path: gr_cv21_ta_30/train-* - config_name: gr_cv21_th_30 data_files: - split: train path: gr_cv21_th_30/train-* - config_name: gr_cv21_vi_30 data_files: - split: train path: gr_cv21_vi_30/train-* - config_name: gr_cv21_zh_30 data_files: - split: train path: gr_cv21_zh_30/train-* - config_name: gr_emota_ta_30 data_files: - split: train path: gr_emota_ta_30/train-* - config_name: gr_fleurs_en_30 data_files: - split: train path: gr_fleurs_en_30/train-* - config_name: gr_fleurs_km_30 data_files: - split: train path: gr_fleurs_km_30/train-* - config_name: gr_indowave_id_30 data_files: - split: train path: gr_indowave_id_30/train-* - config_name: gr_m3ed_30 data_files: - split: train path: gr_m3ed_30/train-* - config_name: gr_openslr_ta_30 data_files: - split: train path: gr_openslr_ta_30/train-* - config_name: gr_sfdusc_30 data_files: - split: train path: gr_sfdusc_30/train-* - config_name: gr_sg_streets_utterance_30 data_files: - split: train path: gr_sg_streets_utterance_30/train-* - config_name: gr_smaldusc_30 data_files: - split: train path: gr_smaldusc_30/train-* - config_name: gr_thai_elderly_th_30 data_files: - split: train path: gr_thai_elderly_th_30/train-* - config_name: gr_thai_ser_th_30 data_files: - split: train path: gr_thai_ser_th_30/train-* - config_name: gr_vietnam_celeb_30 data_files: - split: train path: gr_vietnam_celeb_30/train-* license: cc-by-nc-nd-4.0 task_categories: - audio-classification language: - en - zh - vi - th - id - ms - tl - ta - km tags: - speech - audio-llm - paralinguistics - emotion-recognition - gender-recognition - age-estimation - benchmark - southeast-asia pretty_name: SEA-SpeechBench — Paralinguistics (AGE, ER, GR) size_categories: - 10K=4.0`, which decodes audio through `torchcodec` and needs a system FFmpeg (versions 4–7): ```bash pip install "datasets[audio]" ``` ```python from datasets import load_dataset ds = load_dataset( "MERaLiON/sea_audiobench_datasets_Paralinguistics", "er_esd_en_30", split="train", ) row = ds[0] print(row["instruction"]) print(row["answer"]) print(row["language"], row["audio_length"]) # `context` is a torchcodec AudioDecoder, not a dict samples = row["context"].get_all_samples() waveform = samples.data # torch.Tensor, shape (num_channels, num_samples) sr = samples.sample_rate # 16000 print(waveform.shape, sr) ``` If you need a numpy array — most feature extractors take one — squeeze the channel dimension (all audio here is mono): ```python audio = samples.data.squeeze(0).numpy() ``` ## Subsets All data lives in a single `train` split and is intended for **evaluation only**. Config names follow `{task}_{source}[_{language}]_30`, where the task prefix is `age`, `er`, or `gr`. ### AGE — age recognition | Config | Source | Lang | Examples | Audio (hr) | |---|---|---|---|---| | `age_cv21_en_30` | Common Voice 21 | en | 1,000 | 1.6 | | `age_cv21_ta_30` | Common Voice 21 | ta | 1,000 | 1.5 | | `age_cv21_th_30` | Common Voice 21 | th | 775 | 1.0 | | `age_cv21_vi_30` | Common Voice 21 | vi | 833 | 0.8 | | `age_cv21_zh_30` | Common Voice 21 | zh | 1,000 | 1.6 | | **Subtotal** | | | **4,608** | **~6.6** | ### ER — emotion recognition | Config | Source | Lang | Examples | Audio (hr) | |---|---|---|---|---| | `er_emota_ta_30` | EmoTa | ta | 936 | 0.7 | | `er_esd_en_30` | ESD | en | 1,000 | 0.8 | | `er_esd_zh_30` | ESD | zh | 1,000 | 0.9 | | `er_indowave_id_30` | IndoWaveSentiment | id | 300 | 0.3 | | `er_m3ed_30` | M3ED | zh | 1,000 | 0.4 | | `er_tec_ta_30` | TEC | ta | 165 | 0.7 | | `er_thai_ser_th_30` | THAI SER | th | 955 | 1.6 | | **Subtotal** | | | **5,356** | **~5.3** | ### GR — gender recognition | Config | Source | Lang | Examples | Audio (hr) | |---|---|---|---|---| | `gr_cv21_en_30` | Common Voice 21 | en | 1,000 | 1.6 | | `gr_cv21_id_30` | Common Voice 21 | id | 1,000 | 1.1 | | `gr_cv21_ta_30` | Common Voice 21 | ta | 1,000 | 1.5 | | `gr_cv21_th_30` | Common Voice 21 | th | 747 | 0.9 | | `gr_cv21_vi_30` | Common Voice 21 | vi | 765 | 0.8 | | `gr_cv21_zh_30` | Common Voice 21 | zh | 1,000 | 1.7 | | `gr_emota_ta_30` | EmoTa | ta | 936 | 0.7 | | `gr_fleurs_en_30` | FLEURS | en | 647 | 1.8 | | `gr_fleurs_km_30` | FLEURS | km | 765 | 3.1 | | `gr_indowave_id_30` | IndoWaveSentiment | id | 300 | 0.3 | | `gr_m3ed_30` | M3ED | zh | 1,000 | 0.4 | | `gr_openslr_ta_30` | OpenSLR | ta | 1,000 | 1.7 | | `gr_sfdusc_30` | ASR-SFDuSC | tl | 1,000 | 1.2 | | `gr_sg_streets_utterance_30` | SG Streets | en | 492 | 0.7 | | `gr_smaldusc_30` | ASR-SMalDuSC | ms | 1,000 | 2.1 | | `gr_thai_elderly_th_30` | Thai Elderly Speech | th | 992 | 1.4 | | `gr_thai_ser_th_30` | THAI SER | th | 955 | 1.6 | | `gr_vietnam_celeb_30` | Vietnam-Celeb | vi | 1,000 | 2.1 | | **Subtotal** | | | **15,599** | **~24.9** | Across all three tasks: **25,563 examples, ~36.8 hours.** ## Data fields | Field | Type | Description | |---|---|---| | `context` | `Audio(sampling_rate=16000)` | The audio recording. | | `instruction` | `string` | The instruction given to the model. | | `answer` | `string` | The reference answer. | | `audio_length` | `float64` | Duration of `context`, in seconds. | | `language` | `string` | Language code of the subset. | See the paper for the instruction and answer formats, the label sets used for each task, and the evaluation protocol. ## Source data These tasks introduce no new recordings. All audio comes from existing corpora; the contribution is the utterance selection, instructions, and reference answers. | Source | Tasks | Lang | Upstream license | |---|---|---|---| | Common Voice 21 | AGE, GR | en, id, ta, th, vi, zh | MPL 2.0 | | EmoTa | ER, GR | ta | EACL | | ESD | ER | en, zh | MIT | | FLEURS | GR | en, km | CC BY 4.0 | | IndoWaveSentiment | ER, GR | id | CC BY 4.0 | | M3ED | ER, GR | zh | CC BY-NC-ND 4.0 | | OpenSLR | GR | ta | CC BY-SA 4.0 | | ASR-SFDuSC | GR | tl | CC BY-NC-ND 4.0 | | SG Streets | GR | en | Not specified | | ASR-SMalDuSC | GR | ms | CC BY-NC-ND 4.0 | | TEC | ER | ta | Not specified | | Thai Elderly Speech | GR | th | CC BY-SA 4.0 | | THAI SER | ER, GR | th | CC BY-SA 4.0 | | Vietnam-Celeb | GR | vi | CC BY 4.0 | ## Citation If you use this dataset, please cite our benchmark **and** the source corpora. ### This benchmark ```bibtex @inproceedings{liao2026seaspeechbench, title = {{SEA-SpeechBench}: A Large-Scale Multitask Benchmark for Speech Understanding Across Southeast Asia}, author = {Liao, Jingyi and Zhang, Wenyu and Liu, Zhuohan and He, Yingxu and Lin, Geyu and Zou, Xunlong and Sun, Shuo and Alsagoff, Syed Ali Redha and Aw, Ai Ti}, booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2026} } ``` ### Source corpora ```bibtex @inproceedings{commonvoice, title = {Common Voice: A Massively-Multilingual Speech Corpus}, author = {Ardila, Rosana and Branson, Megan and Davis, Kelly and Kohler, Michael and Meyer, Josh and Henretty, Michael and Morais, Reuben and Saunders, Lindsay and Tyers, Francis and Weber, Gregor}, booktitle = {Conference on Language Resources and Evaluation}, year = {2020} } @inproceedings{emota, title = {EmoTa: A Tamil Emotional Speech Dataset}, author = {Thevakumar, Jubeerathan and Thavarasa, Luxshan and Sivatheepan, Thanikan and Kugarajah, Sajeev and Thayasivam, Uthayasanker}, booktitle = {Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)}, pages = {193--201}, month = {January}, year = {2025}, address = {Abu Dhabi, UAE}, publisher = {International Committee on Computational Linguistics}, url = {https://aclanthology.org/2025.chipsal-1.19/} } @article{esd, title = {Emotional voice conversion: Theory, databases and ESD}, author = {Zhou, Kun and Sisman, Berrak and Liu, Rui and Li, Haizhou}, journal = {Speech Communication}, volume = {137}, pages = {1--18}, year = {2022}, publisher = {Elsevier} } @inproceedings{fleurs, title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech}, author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur}, booktitle = {IEEE Spoken Language Technology Workshop}, year = {2022} } @misc{IndoWaveSentiment, title = {IndoWaveSentiment: Indonesian Audio Dataset for Emotion Classification}, author = {Bustamin, Anugrayani and Rizky, Andi M. and Warni, Elly and Sari Areni, Intan and Indrabayu, Indrabayu}, year = {2024}, howpublished = {Mendeley Data, Version 1}, publisher = {Universitas Hasanuddin}, doi = {10.17632/j9ytfdzy27.1}, url = {https://data.mendeley.com/datasets/j9ytfdzy27/1} } @inproceedings{m3ed, title = {M3ED: Multi-modal multi-scene multi-label emotional dialogue database}, author = {Zhao, Jinming and Zhang, Tenggan and Hu, Jingwen and Liu, Yuchen and Jin, Qin and Wang, Xinchao and Li, Haizhou}, booktitle = {Annual Meeting of the Association for Computational Linguistics}, year = {2022} } @inproceedings{OpenSLR2, title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}}, author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, pages = {6494--6503}, month = may, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.800} } @misc{sfdusc_ph, title = {ASR-SFDuSC: A Scripted Filipino Daily-use Speech Corpus}, author = {{Magic Data Technology}}, year = {2023}, howpublished = {MagicHub}, url = {https://magichub.com/datasets/filipino-scripted-speech-corpus-daily-use-sentence/} } @inproceedings{sg_streets, title = {Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation}, author = {Khassanov, Yerbolat and Zeng, Zhiping and Pham, Van Tung and Xu, Haihua and Chng, Eng Siong}, booktitle = {Interspeech 2019}, pages = {3505--3509}, month = sep, year = {2019}, publisher = {ISCA}, doi = {10.21437/Interspeech.2019-1858} } @misc{SMalDuSC, title = {{ASR-SMalDuSC}: A Scripted Malay Daily-use Speech Corpus}, author = {{Magic Data Technology}}, howpublished = {\url{https://magichub.com/datasets/malay-scripted-speech-corpus-daily-use-sentence/}}, year = {2023} } @misc{tec, title = {tamil-audio-emotion-classification}, author = {Thanushs25}, year = {2024}, howpublished = {\url{https://huggingface.co/datasets/Thanushs25/tamil-audio-emotion-classification}} } @misc{thaielderly, title = {Thai Elderly Speech dataset by Data Wow and VISAI}, author = {{VISAI AI Company Limited} and {Data Wow Company Limited}}, year = {2022}, howpublished = {\url{https://github.com/VISAI-DATAWOW/Thai-Elderly-Speech-dataset/releases/tag/v1.0.0}} } @article{thaiser, title = {THAI Speech Emotion Recognition (THAI-SER) corpus}, author = {Wongpithayadisai, Jilamika and Chaksangchaichot, Chompakorn and Sangnark, Soravitt and Prakrankamanant, Patawee and Gangwanpongpun, Krit and Boonpunmongkol, Siwa and Milindasuta, Premmarin and Na-Pombejra, Dangkamon and Nutanong, Sarana and Chuangsuwanich, Ekapol}, journal = {arXiv preprint arXiv:2507.09618}, year = {2025} } @inproceedings{vietnam_celeb, title = {{Vietnam-Celeb: a large-scale dataset for Vietnamese speaker recognition}}, author = {Pham, Viet Thanh and Nguyen, Xuan Thai Hoa and Hoang, Vu and Nguyen, Thi Thu Trang}, booktitle = {Proc. INTERSPEECH 2023}, pages = {1918--1922}, year = {2023}, doi = {10.21437/Interspeech.2023-1989} } ```