Real-Time System for Audio-Visual Target Speech Enhancement
- URL: http://arxiv.org/abs/2509.20741v1
- Date: Thu, 25 Sep 2025 04:45:28 GMT
- Title: Real-Time System for Audio-Visual Target Speech Enhancement
- Authors: T. Aleksandra Ma, Sile Yin, Li-Chia Yang, Shuo Zhang,
- Abstract summary: We present a real-time audio-visual speech enhancement system designed to run entirely on a CPU.<n>RAVEN fills this gap by using pretrained visual embeddings from an audio-visual speech recognition model to encode lip movement information.<n>In this demonstration, attendees will be able to experience live audio-visual target speech enhancement using a microphone and webcam setup, with clean speech playback through headphones.
- Score: 4.750468009386675
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a live demonstration for RAVEN, a real-time audio-visual speech enhancement system designed to run entirely on a CPU. In single-channel, audio-only settings, speech enhancement is traditionally approached as the task of extracting clean speech from environmental noise. More recent work has explored the use of visual cues, such as lip movements, to improve robustness, particularly in the presence of interfering speakers. However, to our knowledge, no prior work has demonstrated an interactive system for real-time audio-visual speech enhancement operating on CPU hardware. RAVEN fills this gap by using pretrained visual embeddings from an audio-visual speech recognition model to encode lip movement information. The system generalizes across environmental noise, interfering speakers, transient sounds, and even singing voices. In this demonstration, attendees will be able to experience live audio-visual target speech enhancement using a microphone and webcam setup, with clean speech playback through headphones.
Related papers
- Real-Time Audio-Visual Speech Enhancement Using Pre-trained Visual Representations [5.130705720747573]
This paper presents a real-time audio-visual speech enhancement (AVSE) system, RAVEN.<n>It isolates and enhances the on-screen target speaker while suppressing interfering speakers and background noise.<n>To our knowledge, this is the first open-source implementation of a real-time AVSE system.
arXiv Detail & Related papers (2025-07-29T02:38:56Z) - AdVerb: Visually Guided Audio Dereverberation [49.958724234969445]
We present AdVerb, a novel audio-visual dereverberation framework.
It uses visual cues in addition to the reverberant sound to estimate clean audio.
arXiv Detail & Related papers (2023-08-23T18:20:59Z) - Speech inpainting: Context-based speech synthesis guided by video [29.233167442719676]
This paper focuses on the problem of audio-visual speech inpainting, which is the task of synthesizing the speech in a corrupted audio segment.
We present an audio-visual transformer-based deep learning model that leverages visual cues that provide information about the content of the corrupted audio.
We also show how visual features extracted with AV-HuBERT, a large audio-visual transformer for speech recognition, are suitable for synthesizing speech.
arXiv Detail & Related papers (2023-06-01T09:40:47Z) - LA-VocE: Low-SNR Audio-visual Speech Enhancement using Neural Vocoders [53.30016986953206]
We propose LA-VocE, a new two-stage approach that predicts mel-spectrograms from noisy audio-visual speech via a transformer-based architecture.
We train and evaluate our framework on thousands of speakers and 11+ different languages, and study our model's ability to adapt to different levels of background noise and speech interference.
arXiv Detail & Related papers (2022-11-20T15:27:55Z) - Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement
by Re-Synthesis [67.73554826428762]
We propose a novel audio-visual speech enhancement framework for high-fidelity telecommunications in AR/VR.
Our approach leverages audio-visual speech cues to generate the codes of a neural speech, enabling efficient synthesis of clean, realistic speech from noisy signals.
arXiv Detail & Related papers (2022-03-31T17:57:10Z) - Learning Audio-Visual Dereverberation [87.52880019747435]
Reverberation from audio reflecting off surfaces and objects in the environment not only degrades the quality of speech for human perception, but also severely impacts the accuracy of automatic speech recognition.
Our idea is to learn to dereverberate speech from audio-visual observations.
We introduce Visually-Informed Dereverberation of Audio (VIDA), an end-to-end approach that learns to remove reverberation based on both the observed sounds and visual scene.
arXiv Detail & Related papers (2021-06-14T20:01:24Z) - Learning Speech Representations from Raw Audio by Joint Audiovisual
Self-Supervision [63.564385139097624]
We propose a method to learn self-supervised speech representations from the raw audio waveform.
We train a raw audio encoder by combining audio-only self-supervision (by predicting informative audio attributes) with visual self-supervision (by generating talking faces from audio)
Our results demonstrate the potential of multimodal self-supervision in audiovisual speech for learning good audio representations.
arXiv Detail & Related papers (2020-07-08T14:07:06Z) - Visually Guided Self Supervised Learning of Speech Representations [62.23736312957182]
We propose a framework for learning audio representations guided by the visual modality in the context of audiovisual speech.
We employ a generative audio-to-video training scheme in which we animate a still image corresponding to a given audio clip and optimize the generated video to be as close as possible to the real video of the speech segment.
We achieve state of the art results for emotion recognition and competitive results for speech recognition.
arXiv Detail & Related papers (2020-01-13T14:53:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.