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Found 19 papers, 6 papers with code
PAM: Prompting Audio-Language Models for Audio Quality Assessment
Here, we exploit this capability and introduce PAM, a no-reference metric for assessing audio quality for different audio processing tasks.
NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription
The challenge focuses on distant speaker diarization and automatic speech recognition (DASR) in far-field meeting scenarios, with single-channel and known-geometry multi-channel tracks, and serves as a launch platform for two new datasets: First, a benchmarking dataset of 315 meetings, averaging 6 minutes each, capturing a broad spectrum of real-world acoustic conditions and conversational dynamics.
Prompting Audios Using Acoustic Properties For Emotion Representation
In this work, we address the challenge of automatically generating these prompts and training a model to better learn emotion representations from audio and prompt pairs.
Training Audio Captioning Models without Audio
During inference, the text encoder is replaced with the pretrained CLAP audio encoder.
Pengi: An Audio Language Model for Audio Tasks
We introduce Pengi, a novel Audio Language Model that leverages Transfer Learning by framing all audio tasks as text-generation tasks.
Real-Time Audio-Visual End-to-End Speech Enhancement
In this paper, we propose a low-latency real-time audio-visual end-to-end enhancement (AV-E3Net) model based on the recently proposed end-to-end enhancement network (E3Net).
Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling
We propose a cross-lingual neural codec language model, VALL-E X, for cross-lingual speech synthesis.
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
In addition, we find Vall-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis.
Learning to mask: Towards generalized face forgery detection
Our goal is to reduce the features that are easy to learn in the training phase, so as to reduce the risk of overfitting on specific forgery types.
General GAN-generated image detection by data augmentation in fingerprint domain
To our knowledge, we are the first to conduct data augmentation in the fingerprint domain.
Describing emotions with acoustic property prompts for speech emotion recognition
We investigate how the model can learn to associate the audio with the descriptions, resulting in performance improvement of Speech Emotion Recognition and Speech Audio Retrieval.
Real-Time Joint Personalized Speech Enhancement and Acoustic Echo Cancellation
Personalized speech enhancement (PSE) is a real-time SE approach utilizing a speaker embedding of a target person to remove background noise, reverberation, and interfering voices.
Audio Retrieval with WavText5K and CLAP Training
In this work, we propose a new collection of web audio-text pairs and a new framework for retrieval.
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation
Our results show that E3Net provides better speech and transcription quality with a lower target speaker over-suppression (TSOS) rate than the baseline model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Experimental results show that the proposed geometry agnostic model outperforms the model trained on a specific microphone array geometry in both speech quality and automatic speech recognition accuracy.
Personalized Speech Enhancement: New Models and Comprehensive Evaluation
Our results show that the proposed models can yield better speech recognition accuracy, speech intelligibility, and perceptual quality than the baseline models, and the multi-task training can alleviate the TSOS issue in addition to improving the speech recognition accuracy.
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement
Performance analysis is also carried out by changing the ASR model, the data used for the ASR-step, and the schedule of the two update steps.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Advances in Online Audio-Visual Meeting Transcription
This increases marginally to 1. 6% when 50% of the attendees are unknown to the system.
Attention-Aware Compositional Network for Person Re-identification
AACN consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC).
