1 citations · 1 across the 44 of their papers we have counts for
46 papers
Enhancing Neural Speech Coding with Semantic and Visual Cues
Yao Guo, Yang Ai, Hui-Peng Du +3
At low bitrates, neural speech codecs have limited capacity to encode all information needed for high-quality re construction, especially when relying solely on speech-derived repr…
Deep Neural Compression for RIR-Characterized Acoustic Environments with Structure-Aware Constraints
Chen-Yuan Ning, Yang Ai, Hui-Peng Du +2
Room impulse responses (RIRs) characterize the acoustic environment of a room by capturing how sound propagates and decays within an enclosed space. In applications such as immersi…
StreamWSR: Streamable and Lightweight Waveform-Domain Neural Speech Super-Resolution
Yuan Tian, Yang Ai, Hui-Peng Du +1
This paper proposes StreamWSR, a Streamable neural Waveform-domain model for speech Super-Resolution (SR). By adopting a fully causal architecture with compact frame-level waveform…
PACodec: A Low-bitrate Neural Speech Codec with Parallel Additive Vector Quantization
Fei Liu, Yang Ai, Xiao-Hang Jiang +1
This paper proposes PACodec, a novel low-bitrate neural speech codec based on parallel additive vector quantization (PAVQ). Unlike the mainstream residual vector quantization (RVQ)…
Neural Music Enhancement with Dual Time-Frequency Spectral Representations for Prediction and Discrimination
Fei Liu, Yang Ai, Zhen-Hua Ling
Non-professional music recordings shared online often suffer from background noise and reverberation, degrading perceived quality and limiting reuse. This paper proposes DSME, a mu…
Is Semantics Enough for Speech Mean Opinion Score Prediction?
Tianyu Lan, Yufei Shi, Yang Ai +3
Mean Opinion Score (MOS) is the gold standard for evaluating synthesized speech naturalness. However, current automatic MOS predictors are dominated by self-supervised learning (SS…
Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis via Disentangled Audio Infilling
Ye-Xin Lu, Xin Wang, Yang Ai +3
Recent zero-shot text-to-speech (TTS) systems achieve remarkable naturalness and speaker similarity but typically require high-quality speaker prompts and either strip away or enta…
CoSTA: Cognitive-State-Conditioned TTS Data Augmentation Using ASR Transcripts for Alzheimer's Disease Detection
Yin-Long Liu, Yuanchao Li, Yiming Wang +8
Speech-based Alzheimer's Disease (AD) detection is constrained by scarce pathological speech data. To address this, we propose CoSTA, a Text-to-Speech (TTS)-based data augmentation…
Beyond WER: A Paired Acoustic Stress Test for Ambient Clinical Scribes
Xiao-Hang Jiang, Han-Jie Guo, Ying-Si Liang +4
Ambient clinical scribes increasingly combine Automatic Speech Recognition with Large Language Models to automate documentation. However, traditional metrics like Word Error Rate m…
VoCodec: A Low-bitrate Streamable Neural Speech Codec with Voicing-driven Quantization
Xiao-Hang Jiang, Yang Ai, Rui-Chen Zheng +3
Neural speech codecs are key to speech transmission and storage, but most use uniform quantization across frames, allocating the same bitrate regardless of content and wasting bits…
An Ultra-Low-Bitrate Neural Speech Codec with Plain-to-Pseudo Synergistic Vector Quantization
Xiao-Hang Jiang, Yang Ai, Fei Liu +4
Most neural speech codecs use residual vector quantization (RVQ), in which later VQs contribute less but consume the same bitrate, leading to inefficiency. We propose P2PSynCodec,…
CFMDCTCodec: A Low-Bitrate Neural Speech Codec with Noise-Prior-aware Conditional Flow Matching for MDCT-Spectral Enhancement
Xiao-Hang Jiang, Yang Ai, Hui-Peng Du +2
High-quality speech coding at low bitrates is crucial for bandwidth-constrained applications, yet remains challenging due to the severe loss of quality-critical information in high…
Ultra-Low-Bitrate Mel-Spectrogram-based Neural Speech Coding with Flow-Matching-based Refinement and Vocoding-driven Reconstruction
Hui-Peng Du, Yang Ai, Xiao-Hang Jiang +2
Ultra-low-bitrate speech coding is pivotal for bandwidth-constrained communication and deep compression, yet maintaining naturalness and speaker identity at such extreme bit budget…
LatentFlowSR: High-Fidelity Audio Super-Resolution via Noise-Robust Latent Flow Matching
Fei Liu, Yang Ai, Hui-Peng Du +2
Audio super-resolution aims to recover missing high-frequency details from bandwidth-limited low-resolution audio, thereby improving the naturalness and perceptual quality of the r…
CodeSep: Low-Bitrate Codec-Driven Speech Separation with Base-Token Disentanglement and Auxiliary-Token Serial Prediction
Hui-Peng Du, Yang Ai, Xiao-Hang Jiang +2
This paper targets a new scenario that integrates speech separation with speech compression, aiming to disentangle multiple speakers while producing discrete representations for ef…
Universal Discrete-Domain Speech Enhancement
Fei Liu, Yang Ai, Ye-Xin Lu +3
In real-world scenarios, speech signals are inevitably corrupted by various types of interference, making speech enhancement (SE) a critical task for robust speech processing. Howe…
Understanding Textual Capability Degradation in Speech LLMs via Parameter Importance Analysis
Chao Wang, Rui-Chen Zheng, Yang Ai +1
The integration of speech into Large Language Models (LLMs) has substantially expanded their capabilities, but often at the cost of weakening their core textual competence. This de…
Neural Speech Separation with Parallel Amplitude and Phase Spectrum Estimation
Fei Liu, Yang Ai, Zhen-Hua Ling
This paper proposes APSS, a novel neural speech separation model with parallel amplitude and phase spectrum estimation. Unlike most existing speech separation methods, the APSS dis…
A High-Quality and Low-Complexity Streamable Neural Speech Codec with Knowledge Distillation
En-Wei Zhang, Hui-Peng Du, Xiao-Hang Jiang +2
While many current neural speech codecs achieve impressive reconstructed speech quality, they often neglect latency and complexity considerations, limiting their practical deployme…
A Distilled Low-Latency Neural Vocoder with Explicit Amplitude and Phase Prediction
Hui-Peng Du, Yang Ai, Zhen-Hua Ling
The majority of mainstream neural vocoders primarily focus on speech quality and generation speed, while overlooking latency, which is a critical factor in real-time applications.…
DAIEN-TTS: Disentangled Audio Infilling for Environment-Aware Text-to-Speech Synthesis
Ye-Xin Lu, Yu Gu, Kun Wei +3
This paper presents DAIEN-TTS, a zero-shot text-to-speech (TTS) framework that enables ENvironment-aware synthesis through Disentangled Audio Infilling. By leveraging separate spea…
Say More with Less: Variable-Frame-Rate Speech Tokenization via Adaptive Clustering and Implicit Duration Coding
Rui-Chen Zheng, Wenrui Liu, Hui-Peng Du +6
Existing speech tokenizers typically assign a fixed number of tokens per second, regardless of the varying information density or temporal fluctuations in the speech signal. This u…
Is GAN Necessary for Mel-Spectrogram-based Neural Vocoder?
Hui-Peng Du, Yang Ai, Rui-Chen Zheng +2
Recently, mainstream mel-spectrogram-based neural vocoders rely on generative adversarial network (GAN) for high-fidelity speech generation, e.g., HiFi-GAN and BigVGAN. However, th…
Universal Preference-Score-based Pairwise Speech Quality Assessment
Yu-Fei Shi, Yang Ai, Zhen-Hua Ling
To compare the performance of two speech generation systems, one of the most effective approaches is estimating the preference score between their generated speech. This paper prop…