I am a Postdoctoral Scholar– NSTC Fellow at the Signal Analysis and Interpretation Laboratory (SAIL) at the University of Southern California, working with Prof. Shrikanth S. Narayanan. I received my Ph.D. in Electrical Engineering from National Tsing Hua University, advised by Prof. Chi-Chun Lee.
My research centers on three threads: (1) speech emotion recognition under subjectivity and ambiguity— multi-label learning, annotator disagreement, calibration, fairness, and open evaluation via EMO-SUPERB and a survey of bias and fairness in speech AI; (2) conversational social signal processing—deception and belongingness in dialogue / small groups, and dyadic clinical affect such as depression-related speech; and (3) speech systems for assistants and multilingual / low-resource settings—unified ASR + SER, speech LLMs, expressive TTS evaluation, edge SER / KWS, and collaborations such as TaigiSpeech. At USC SAIL / SPAN, I also work on speech enhancement for real-time MRI (rtMRI).
I have worked on speech and ML systems from research through deployment at Amazon Alexa, ITRI, and RealTek. I am motivated by building intelligent, expressive agents that integrate speech, language, and emotion for natural interaction. I am currently seeking full-time research and industry positions.
Grouped by research theme. Selected papers under each; complete list on the CV.
USC SAIL · SPAN
Contribution to SAIL’s Speech Production and Articulation Knowledge (SPAN) effort on real-time MRI of dynamic vocal-tract shaping. First-author work evaluates modern speech enhancement on rtMRI speech—objective quality plus downstream tasks (ASR, speaker, SER, demographics)— and releases practical guidance with an open demo so SPAN corpora are more usable beyond speech science. Preprint on arXiv (submitted to JASA).
Theme
Modeling annotator disagreement, minority views, soft labels, and calibration so SER systems respect emotion ambiguity rather than collapsing to a single “ground truth.”
Theme
Affect and social meaning in dialogue: deception and perceived lying in interactive games, belongingness / satisfaction in small-group conversation, multimodal group emotion, and dyadic depression-related speech (including mentored work).
Theme
Subgroup disparity, debiasing without fragile demographic shortcuts, and confidence-oriented augmentation for fairer multi-label emotion recognition—plus a survey that maps fairness definitions to evaluation and mitigation across speech AI.
Open evaluation
Reproducible evaluation contracts for classical and speech-LLM SER: splits, metrics, prompts, parsing, and diagnostics so results stay auditable.
Theme
Unified ASR + multi-label SER for voice-assistant workloads, emotional-speech ASR analysis, stimulus-modality effects, and on-device SER / KWS under compute budgets.
Theme
Large audio / speech language models, expressive TTS evaluation, codec emotion preservation, and low-resource speech resources such as TaigiSpeech.
Full list on the CV and Google Scholar (722 citations; h-index 15; i10-index 23, 29 Aug 2026). Name in bold indicates Huang-Cheng Chou.
Full awards, reviewing, mentoring, and publication list are on the CV page.
Currently seeking full-time roles. Open to conversations on speech AI, voice assistants, and research collaboration.
