13 citations · 13 across the 5 of their papers we have counts for
7 papers
Qwen-Audio-3.1-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +45
We present Qwen-Audio-3.1-ASR, a family of audio-language models that builds upon its predecesor Qwen-Audio-3.0-ASR with improved speech recognition and expanded capabilities in sp…
Qwen-Audio-3.0-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +42
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model scaling, and deep integra…
HPRO: Hierarchical Progressive Reward Optimization via Preference Extraction for Emotional Text-to-Speech
Sihang Nie, Xiaofen Xing, Rui Xing +5
Recently, Large Language Model (LLM)-based Text-to-Speech (TTS) models have achieved remarkable naturalness. However, the standard Supervised Fine-Tuning paradigm often converges t…
HD-PPT: Hierarchical Decoding of Content- and Prompt-Preference Tokens for Instruction-based TTS
Sihang Nie, Xiaofen Xing, Jingyuan Xing +2
Large Language Model (LLM)-based Text-to-Speech (TTS) models have already reached a high degree of naturalness. However, the precision control of TTS inference is still challenging…
FLAP: Fully-controllable Audio-driven Portrait Video Generation through 3D head conditioned diffusion model
Lingzhou Mu, Baiji Liu, Ruonan Zhang +4
Diffusion-based video generation techniques have significantly improved zero-shot talking-head avatar generation, enhancing the naturalness of both head motion and facial expressio…
CB-Conformer: Contextual biasing Conformer for biased word recognition
Yaoxun Xu, Baiji Liu, Qiaochu Huang and +4
Due to the mismatch between the source and target domains, how to better utilize the biased word information to improve the performance of the automatic speech recognition model in…
Multi-head Monotonic Chunkwise Attention For Online Speech Recognition
Baiji Liu, Songjun Cao, Sining Sun +2
The attention mechanism of the Listen, Attend and Spell (LAS) model requires the whole input sequence to calculate the attention context and thus is not suitable for online speech…