CleanCodec reframes audio tokenization as a selective information bottleneck to encode only perceptually important features at 12.5 tokens per second, outperforming prior codecs in efficiency, speaker similarity, and intelligibility.
Reshape Dimensions Network for Speaker Recognition
4 Pith papers cite this work, alongside 28 external citations. Polarity classification is still indexing.
4
Pith papers citing it
28
external citations · OpenAlex
fields
cs.SD 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
SpeakerLLM unifies speaker profiling, recording-condition understanding, and structured verification reasoning in an audio-LLM via a hierarchical tokenizer and decision traces.
Post-processing with an encoder-decoder model yields 22% relative EER reduction on normal-vs-whispered trials and 1.88% EER on whispered-vs-whispered, outperforming ReDimNet-B2.
Kiwano is an open-source toolkit that supplies standardized PyTorch pipelines, pretrained models, and evaluation protocols for speaker verification.
citing papers explorer
-
CleanCodec: Efficient and Robust Speech Tokenization via Perceptually Guided Encoding
CleanCodec reframes audio tokenization as a selective information bottleneck to encode only perceptually important features at 12.5 tokens per second, outperforming prior codecs in efficiency, speaker similarity, and intelligibility.
-
SpeakerLLM: A Speaker-Specialized Audio-LLM for Speaker Understanding and Verification Reasoning
SpeakerLLM unifies speaker profiling, recording-condition understanding, and structured verification reasoning in an audio-LLM via a hierarchical tokenizer and decision traces.
-
Enhancing Speaker Verification with Whispered Speech via Post-Processing
Post-processing with an encoder-decoder model yields 22% relative EER reduction on normal-vs-whispered trials and 1.88% EER on whispered-vs-whispered, outperforming ReDimNet-B2.
-
Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification
Kiwano is an open-source toolkit that supplies standardized PyTorch pipelines, pretrained models, and evaluation protocols for speaker verification.