16 papers
Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: Exploration toward Age, Gender, and Accent Steering
Shih-Heng Wang, Tiantian Feng, Aditya Kommineni +4
Neural audio codecs (NACs) are widely used in speech generation and audio-language modeling, yet how they encode speaker-trait information remains poorly understood. Prior work app…
Audio Preprocessing Effects on Stuttering Detection: A Class-Specific Analysis
Anisha Pattanayak, Hanie Kang, Huang-Cheng Chou +1
Audio preprocessing can affect how well a system detects stuttering. We study a simulated chain of denoising,loudness normalisation, Opus coding, and voice activity detection on SE…
Navigating Speech Enhancement for Real-Time MRI: A Systematic Assessment of Signal Quality, Source Preservation, and Downstream Tasks
Huang-Cheng Chou, Sean Foley, Haley Hsu +10
Audio recorded during real-time magnetic resonance imaging (rtMRI) is heavily contaminated by scanner noise, but it remains unclear whether general-purpose speech enhancement impro…
AMECxSV: Adaptive Metadata-Driven Embedding-Fusion Calibration for X-Lingual Speaker Verification
Xin Wei, Shi He, Yihe Yuan +3
In X-lingual automatic speaker verification (ASV), fixed front-end scores vary in reliability with language match, duration, and score source. We propose AMECxSV, an adaptive metad…
Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives
Hanie Kang, Huang-Cheng Chou, Sudarsana Reddy Kadiri +1
Automatic depression detection from clinical interviews typically models the semantic content and acoustic characteristics of participant speech. However, the interactional timing…
Layer-wise Cross-Lingual Depression Detection from Speech: Analysis with Contrastive Alignment
Anisha Pattanayak, Hanie Kang, Huang-Cheng Chou +2
Significant disparities exist in the diagnosis and clinical presentation of depression across different linguistic populations. Speech-based depression detection performs well mono…
Speaker-Aware Temporal Aggregation Strategies on Segment Representations for Depression Detection in Dyadic Interaction: A Benchmark Study
Anisha Pattanayak, Huang-Cheng Chou, Shrikanth Narayanan +1
Speech-based depression detection compresses features from short audio segments into one speaker-level decision, a step called temporal aggregation rarely studied on its own. Most…
Speaker Identity in Non-Verbal Vocalizations: Conditional Distillation and Mixture of Experts Approach
Tzu-Chieh Wei, Yi-Cheng Lin, Huang-Cheng Chou +4
As expressive text-to-speech (TTS) and voice conversion (VC) systems increasingly generate non-verbal vocalizations (NVVs) to enhance naturalness, reliable speaker verification (SV…
AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling
Jiacheng Shi, Hongfei Du, Xinyuan Song +3
Neural speech codecs provide discrete representations for speech language models, but emotional cues are often degraded during quantization. Existing codecs mainly optimize acousti…
Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI
Yi-Cheng Lin, Yun-Shao Tsai, Kuan-Yu Chen +6
Speech technologies are deployed in high-stakes settings, yet fairness concerns remain fragmented across tasks and disciplines. Existing surveys either adopt a general machine-lear…
The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation
Yun-Shao Tsai, Yi-Cheng Lin, Huang-Cheng Chou +5
Objective metrics for emotional expressiveness are vital for speech generation, particularly in expressive synthesis and voice conversion requiring emotional prosody transfer. To q…
The Binding Effect: Analyzing How Multi-Dimensional Cues Form Gender Bias in Instruction TTS
Kuan-Yu Chen, Yi-Cheng Lin, Po-Chung Hsieh +5
Current bias evaluations in Instruction Text-to-Speech (ITTS) often rely on univariate testing, overlooking the compositional structure of social cues. In this work, we investigate…
VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs
Hezhao Zhang, Huang-Cheng Chou, Shrikanth Narayanan +1
Speech Large Language Models (LLMs) show great promise for speech emotion recognition (SER) via generative interfaces. However, shifting from closed-set classification to open text…
TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild
Kai-Wei Chang, Yi-Cheng Lin, Huang-Cheng Chou +9
Speech technologies have advanced rapidly and serve diverse populations worldwide. However, many languages remain underrepresented due to limited resources. In this paper, we intro…
AV-EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Omni-modal LLMS with Audio-visual Cues
Dingkun Zhou, Krish Patel, Ajay Kankipati +14
Emotions conveyed through voice and face shape engagement and context in human AI interaction. Despite rapid progress in omni modal large language models, the holistic evaluation o…
EMO-Reasoning: Benchmarking Emotional Reasoning Capabilities in Spoken Dialogue Systems
Jingwen Liu, Kan Jen Cheng, Jiachen Lian +8
Speech emotions play a crucial role in human-computer interaction, shaping engagement and context-aware communication. Despite recent advances in spoken dialogue systems, a holisti…