ClariCodec achieves 3.55% WER on LibriSpeech test-clean at 300 bps by RL fine-tuning the encoder for intelligibility, yielding a 23% relative WER reduction while preserving perceptual quality.
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Bigcodec: Pushing the limits of low-bitrate neural speech codec
11 Pith papers cite this work. Polarity classification is still indexing.
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AffectCodec is an emotion-guided neural speech codec that preserves emotional cues during quantization while maintaining semantic fidelity and prosodic naturalness.
Semantic priors from HuBERT and Whisper improve speech codec intelligibility up to 6 kbps but show diminishing returns beyond that, with a bitrate-aware regulation strategy balancing semantic consistency and naturalness.
LATTE creates a compact latent token bottleneck in audio tokenizers that aggregates global information and enables unsupervised editing of attributes like speaker identity via token swapping.
L3-SE reduces linguistic hallucination in LM-based speech enhancement by distilling noise-invariant acoustic-semantic representations from noisy inputs to condition an autoregressive decoder-only language model.
LLM-Codec augments audio codec training with multi-step token prediction and contrastive semantic alignment to improve both waveform reconstruction and autoregressive predictability for speech language models.
Pupu-Vocoder and Pupu-Codec integrate differentiable anti-aliasing into neural audio models to eliminate aliasing artifacts from non-linear activations and upsampling, yielding better results on music and singing voice.
Q2D2 uses 2D geometric grid projections to quantize feature pairs in neural audio codecs, yielding implicit codebooks that improve efficiency and utilization over RVQ, VQ, and FSQ while maintaining reconstruction quality.
Step-Audio 2 integrates a latent audio encoder, reasoning-centric reinforcement learning, and discrete audio token generation into language modeling to deliver state-of-the-art performance on audio understanding and conversational benchmarks.
HybridCodec combines discrete tokens with continuous residuals via a focal modulation codec and hybrid Transformer to improve speaker retention and reduce autoregressive steps in speech language models.
citing papers explorer
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Optimising Neural Speech Codecs for 300bps Communication using Reinforcement Learning
ClariCodec achieves 3.55% WER on LibriSpeech test-clean at 300 bps by RL fine-tuning the encoder for intelligibility, yielding a 23% relative WER reduction while preserving perceptual quality.
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AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling
AffectCodec is an emotion-guided neural speech codec that preserves emotional cues during quantization while maintaining semantic fidelity and prosodic naturalness.
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SPG-Codec: Exploring the Role and Boundaries of Semantic Priors in Ultra-Low-Bitrate Neural Speech Coding
Semantic priors from HuBERT and Whisper improve speech codec intelligibility up to 6 kbps but show diminishing returns beyond that, with a bitrate-aware regulation strategy balancing semantic consistency and naturalness.
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Exploring Token-Space Manipulation in Latent Audio Tokenizers
LATTE creates a compact latent token bottleneck in audio tokenizers that aggregates global information and enables unsupervised editing of attributes like speaker identity via token swapping.
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Reducing Linguistic Hallucination in LM-Based Speech Enhancement via Noise-Invariant Acoustic-Semantic Distillation
L3-SE reduces linguistic hallucination in LM-based speech enhancement by distilling noise-invariant acoustic-semantic representations from noisy inputs to condition an autoregressive decoder-only language model.
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LLM-Codec: Neural Audio Codec Meets Language Model Objectives
LLM-Codec augments audio codec training with multi-step token prediction and contrastive semantic alignment to improve both waveform reconstruction and autoregressive predictability for speech language models.
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Aliasing-Free Neural Audio Synthesis
Pupu-Vocoder and Pupu-Codec integrate differentiable anti-aliasing into neural audio models to eliminate aliasing artifacts from non-linear activations and upsampling, yielding better results on music and singing voice.
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Two-Dimensional Quantization for Geometry-Aware Audio Coding
Q2D2 uses 2D geometric grid projections to quantize feature pairs in neural audio codecs, yielding implicit codebooks that improve efficiency and utilization over RVQ, VQ, and FSQ while maintaining reconstruction quality.
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Step-Audio 2 Technical Report
Step-Audio 2 integrates a latent audio encoder, reasoning-centric reinforcement learning, and discrete audio token generation into language modeling to deliver state-of-the-art performance on audio understanding and conversational benchmarks.
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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models
HybridCodec combines discrete tokens with continuous residuals via a focal modulation codec and hybrid Transformer to improve speaker retention and reduce autoregressive steps in speech language models.
- PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization