Xuankai Chang

3.0k total citations · 2 hit papers
66 papers, 1.6k citations indexed

About

Xuankai Chang is a scholar working on Artificial Intelligence, Signal Processing and Computational Mechanics. According to data from OpenAlex, Xuankai Chang has authored 66 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Artificial Intelligence, 43 papers in Signal Processing and 2 papers in Computational Mechanics. Recurrent topics in Xuankai Chang's work include Speech Recognition and Synthesis (56 papers), Music and Audio Processing (36 papers) and Speech and Audio Processing (35 papers). Xuankai Chang is often cited by papers focused on Speech Recognition and Synthesis (56 papers), Music and Audio Processing (36 papers) and Speech and Audio Processing (35 papers). Xuankai Chang collaborates with scholars based in United States, China and Japan. Xuankai Chang's co-authors include Shinji Watanabe, Yanmin Qian, Dong Yu, Wangyou Zhang, Jiatong Shi, Jonathan Le Roux, Hung-yi Lee, Yuya Fujita, Shu-Wen Yang and Shang-Wen Li and has published in prestigious journals such as IEEE Signal Processing Magazine, Speech Communication and Computer Speech & Language.

In The Last Decade

Xuankai Chang

62 papers receiving 1.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

SUPERB: Speech Processing Universal PERformance Benchmark

2021 409 citations Jiatong Shi, Xuankai Chang et al. profile →
AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head

2024 48 citations Rongjie Huang, Mingze Li et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Xuankai Chang United States	21	1.4k	1.1k	88	72	70	66	1.6k
Shinnosuke Takamichi Japan	16	756 0.5×	682 0.6×	64 0.7×	115 1.6×	56 0.8×	116	978
Petr Motlíček Switzerland	21	1.1k 0.8×	831 0.8×	98 1.1×	114 1.6×	28 0.4×	155	1.4k
Tom Ko China	13	1.6k 1.1×	1.3k 1.2×	107 1.2×	167 2.3×	31 0.4×	44	1.8k
Tsubasa Ochiai Japan	7	897 0.6×	681 0.6×	45 0.5×	71 1.0×	51 0.7×	11	1.0k
Rohit Sinha India	19	946 0.7×	900 0.8×	112 1.3×	130 1.8×	38 0.5×	123	1.2k
Ehsan Variani United States	11	939 0.7×	884 0.8×	25 0.3×	83 1.2×	63 0.9×	22	1.1k
Pegah Ghahremani United States	12	1.1k 0.8×	901 0.8×	136 1.5×	65 0.9×	16 0.2×	18	1.3k
Shinji Takaki Japan	16	817 0.6×	823 0.7×	56 0.6×	124 1.7×	111 1.6×	50	1.0k
Daniel Erro Spain	16	766 0.6×	765 0.7×	103 1.2×	76 1.1×	41 0.6×	49	879
Rahim Saeidi Finland	21	848 0.6×	1.1k 1.0×	72 0.8×	127 1.8×	234 3.3×	65	1.2k

Countries citing papers authored by Xuankai Chang

Since Specialization

Citations

This map shows the geographic impact of Xuankai Chang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xuankai Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xuankai Chang more than expected).

Fields of papers citing papers by Xuankai Chang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xuankai Chang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xuankai Chang. The network helps show where Xuankai Chang may publish in the future.

Co-authorship network of co-authors of Xuankai Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Xuankai Chang. A scholar is included among the top collaborators of Xuankai Chang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xuankai Chang. Xuankai Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Masuyama, Yoshiki, Xuankai Chang, Wangyou Zhang, et al.. (2025). An end-to-end integration of speech separation and recognition with self-supervised learning representation. Computer Speech & Language. 95. 101813–101813.

2.

Chen, William, Wangyou Zhang, Yifan Peng, et al.. (2024). Towards Robust Speech Representation Learning for Thousands of Languages. 10205–10224. 1 indexed citations

3.

Huang, Rongjie, Mingze Li, Dongchao Yang, et al.. (2024). AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head. Proceedings of the AAAI Conference on Artificial Intelligence. 38(21). 23802–23804. 48 indexed citations breakdown →

4.

Chang, Xuankai, Jiatong Shi, Yihan Wu, et al.. (2024). The Interspeech 2024 Challenge on Speech Processing Using Discrete Units. 2559–2563. 8 indexed citations

5.

Shi, Jiatong, William Chen, Xuankai Chang, et al.. (2024). ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets. 1230–1234. 1 indexed citations

6.

Guo, Pengcheng, Xuankai Chang, Hang Lv, Shinji Watanabe, & Lei Xie. (2024). SQ-Whisper: Speaker-Querying Based Whisper Model for Target-Speaker ASR. IEEE Transactions on Audio Speech and Language Processing. 33. 175–185. 1 indexed citations

7.

Chang, Xuankai, Shinji Watanabe, Marc Delcroix, et al.. (2024). Module-Based End-to-End Distant Speech Processing: A case study of far-field automatic speech recognition. IEEE Signal Processing Magazine. 41(6). 39–50.

8.

Huang, Junwei, et al.. (2023). FindAdaptNet: Find and Insert Adapters by Learned Layer Importance. 3 indexed citations

9.

Cornell, Samuele, Shinji Watanabe, Desh Raj, et al.. (2023). The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios. 1–6. 31 indexed citations

10.

Chen, William, et al.. (2023). Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute. 4404–4408. 10 indexed citations

11.

Chen, William, Wei‐Ping Huang, Xuankai Chang, et al.. (2023). ML-SUPERB: Multilingual Speech Universal PERformance Benchmark. 884–888. 23 indexed citations

12.

Chang, Xuankai, et al.. (2023). Exploration of Efficient End-to-End ASR using Discretized Input from Self-Supervised Learning. 1399–1403. 24 indexed citations

13.

Fujita, Yuya, et al.. (2023). LV-CTC: Non-Autoregressive ASR With CTC and Latent Variable Models. 1–6.

14.

Chen, William, et al.. (2023). A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning. 1528–1532. 7 indexed citations

15.

Peng, Yifan, Siddhant Arora, Yosuke Higuchi, et al.. (2023). A Study on the Integration of Pre-Trained SSL, ASR, LM and SLU Models for Spoken Language Understanding. 406–413. 9 indexed citations

16.

Arora, Siddhant, Siddharth Dalmia, Xuankai Chang, et al.. (2022). Two-Pass Low Latency End-to-End Spoken Language Understanding. Interspeech 2022. 3478–3482. 7 indexed citations

17.

Li, Chenda, Jing Shi, Wangyou Zhang, et al.. (2020). ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration. arXiv (Cornell University). 53 indexed citations

18.

Chang, Xuankai, et al.. (2020). End-to-End ASR with Adaptive Span Self-Attention. 5 indexed citations

19.

Zhang, Wangyou, Xuankai Chang, & Yanmin Qian. (2019). Knowledge Distillation for End-to-End Monaural Multi-Talker ASR System. 2633–2637. 6 indexed citations

20.

Chang, Xuankai, et al.. (2016). Unrestricted Vocabulary Keyword Spotting Using LSTM-CTC. 36 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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