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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…
Vision-Integrated High-Quality Neural Speech Coding
Yao Guo, Yang Ai, Rui-Chen Zheng +3
This paper proposes a novel vision-integrated neural speech codec (VNSC), which aims to enhance speech coding quality by leveraging visual modality information. In VNSC, the image…
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising
Ye-Xin Lu, Hui-Peng Du, Fei Liu +2
Large language model (LLM) based zero-shot text-to-speech (TTS) methods tend to preserve the acoustic environment of the audio prompt, leading to degradation in synthesized speech…
Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining
Lu Dong, Haiyu Zhang, Hongjie Zhang +5
The task of weakly supervised temporal sentence grounding (WSTSG) aims to detect temporal intervals corresponding to a language description from untrimmed videos with only video-le…
A Streamable Neural Audio Codec with Residual Scalar-Vector Quantization for Real-Time Communication
Xiao-Hang Jiang, Yang Ai, Rui-Chen Zheng +1
This paper proposes StreamCodec, a streamable neural audio codec designed for real-time communication. StreamCodec adopts a fully causal, symmetric encoder-decoder structure and op…