Search Results for author:
Found 11 papers, 6 papers with code
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
In this paper, we propose LauraGPT, a unified GPT model for audio recognition, understanding, and generation.
FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec
We also demonstrate that the pre-trained models are suitable for downstream tasks, including automatic speech recognition and personalized text-to-speech synthesis.
CASA-ASR: Context-Aware Speaker-Attributed ASR
In addition, a two-pass decoding strategy is further proposed to fully leverage the contextual modeling ability resulting in a better recognition performance.
FunASR: A Fundamental End-to-End Speech Recognition Toolkit
FunASR offers models trained on large-scale industrial corpora and the ability to deploy them in applications.
Ranked #1 on
Speech Recognition
on WenetSpeech
(using extra training data)
TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization
Recently, end-to-end neural diarization (EEND) is introduced and achieves promising results in speaker-overlapped scenarios.
Ranked #1 on
Speaker Diarization
on CALLHOME
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings
Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings
Therefore, we propose the second approach, WD-SOT, to address alignment errors by introducing a word-level diarization model, which can get rid of such timestamp alignment dependency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios
Through this formulation, we propose the speaker embedding-aware neural diarization (SEND) framework, where a speech encoder, a speaker encoder, two similarity scorers, and a post-processing network are jointly optimized to predict the encoded labels according to the similarities between speech features and speaker embeddings.
Ranked #1 on
Speaker Diarization
on AliMeeting
Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual Information
In this paper, we reformulate this task as a single-label prediction problem by encoding the multi-speaker labels with power set.
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting
To improve the robustness, a speech enhancement front-end is involved.
Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events
In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events.
