8 citations · 11 across the 6 of their papers we have counts for
7 papers
LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR
Yuxuan He, Genshun Wan, Pengcheng Li +3
Code-switching (CS) speech, characterized by language alternation within a single utterance, is prevalent in multilingual communities and poses challenges to automatic speech recog…
Luna-TTS Family Technical Report
Feng Yin, Shuai Shi, Junjie Zheng +19
Modern text-to-speech (TTS) is dominated by autoregressive (AR) codec language models, whose left-to-right decoding brings latency that grows with utterance length, error accumulat…
DenoiseRotator: Enhance Pruning Robustness for LLMs via Importance Concentration
Tianteng Gu, Bei Liu, Bo Xiao +3
Pruning is a widely used technique to compress large language models (LLMs) by removing unimportant weights, but it often suffers from significant performance degradation - especia…
Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification
Bei Liu, Yanmin Qian
Recent speaker verification (SV) systems have shown a trend toward adopting deeper speaker embedding extractors. Although deeper and larger neural networks can significantly improv…
Towards Lightweight Speaker Verification via Adaptive Neural Network Quantization
Bei Liu, Haoyu Wang, Yanmin Qian
Modern speaker verification (SV) systems typically demand expensive storage and computing resources, thereby hindering their deployment on mobile devices. In this paper, we explore…
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
Haoyu Wang, Bei Liu, Hang Shao +4
Parameter quantization for Large Language Models (LLMs) has attracted increasing attentions recently in reducing memory costs and improving computational efficiency. Early approach…
One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models
Hang Shao, Bei Liu, Bo Xiao +3
Various Large Language Models~(LLMs) from the Generative Pretrained Transformer(GPT) family have achieved outstanding performances in a wide range of text generation tasks. However…