7 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
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-Jev: A Unified Interface for Audio Decisions
Peixin Chen, Ying Fang, Zhifu Gao +19
Large audio language models (LALMs) have made substantial progress in audio understanding. However, autoregressive text generation introduces computational overhead and latency in…
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…
Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt
Yanfeng Shi, Pengfei Cai, Jun Liu +5
Large Audio-Language Models (LALMs) enable general audio understanding and demonstrate remarkable performance across various audio tasks. However, these models still face challenge…
Spectrogram features for audio and speech analysis
Ian McLoughlin, Lam Pham, Yan Song +7
Spectrogram-based representations have grown to dominate the feature space for deep learning audio analysis systems, and are often adopted for speech analysis also. Initially, the…