Lip-to-Speech Synthesis in the Wild with Multi-task Learning

• URL: http://arxiv.org/abs/2302.08841v1
• Date: Fri, 17 Feb 2023 12:31:26 GMT
• Title: Lip-to-Speech Synthesis in the Wild with Multi-task Learning
• Authors: Minsu Kim, Joanna Hong, Yong Man Ro
• Abstract summary: We develop a powerful Lip2Speech method that can reconstruct speech with correct contents from the input lip movements, even in a wild environment. We design multi-task learning that guides the model using multimodal supervision, i.e., text and audio, to complement the insufficient word representations of acoustic feature reconstruction loss.
• Score: 32.65865343643458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies have shown impressive performance in Lip-to-speech synthesis that aims to reconstruct speech from visual information alone. However, they have been suffering from synthesizing accurate speech in the wild, due to insufficient supervision for guiding the model to infer the correct content. Distinct from the previous methods, in this paper, we develop a powerful Lip2Speech method that can reconstruct speech with correct contents from the input lip movements, even in a wild environment. To this end, we design multi-task learning that guides the model using multimodal supervision, i.e., text and audio, to complement the insufficient word representations of acoustic feature reconstruction loss. Thus, the proposed framework brings the advantage of synthesizing speech containing the right content of multiple speakers with unconstrained sentences. We verify the effectiveness of the proposed method using LRS2, LRS3, and LRW datasets.

Related papers

• Towards Accurate Lip-to-Speech Synthesis in-the-Wild [31.289366690147556]
  We introduce a novel approach to address the task of synthesizing speech from silent videos of any in-the-wild speaker solely based on lip movements. The traditional approach of directly generating speech from lip videos faces the challenge of not being able to learn a robust language model from speech alone. We propose incorporating noisy text supervision using a state-of-the-art lip-to-text network that instills language information into our model.
  arXiv  Detail & Related papers  (2024-03-02T04:07:24Z)
• DiffV2S: Diffusion-based Video-to-Speech Synthesis with Vision-guided Speaker Embedding [52.84475402151201]
  We present a vision-guided speaker embedding extractor using a self-supervised pre-trained model and prompt tuning technique. We further develop a diffusion-based video-to-speech synthesis model, so called DiffV2S, conditioned on those speaker embeddings and the visual representation extracted from the input video. Our experimental results show that DiffV2S achieves the state-of-the-art performance compared to the previous video-to-speech synthesis technique.
  arXiv  Detail & Related papers  (2023-08-15T14:07:41Z)
• Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping [4.271091833712731]
  We propose a simple approach, named Lip2Vec that is based on learning a prior model. The proposed model compares favorably with fully-supervised learning methods on the LRS3 dataset achieving 26 WER. We believe that reprogramming the VSR as an ASR task narrows the performance gap between the two and paves the way for more flexible formulations of lip reading.
  arXiv  Detail & Related papers  (2023-08-11T12:59:02Z)
• RobustL2S: Speaker-Specific Lip-to-Speech Synthesis exploiting Self-Supervised Representations [13.995231731152462]
  We propose RobustL2S, a modularized framework for Lip-to-Speech synthesis. A non-autoregressive sequence-to-sequence model maps self-supervised visual features to a representation of disentangled speech content. A vocoder then converts the speech features into raw waveforms.
  arXiv  Detail & Related papers  (2023-07-03T09:13:57Z)
• Zero-shot personalized lip-to-speech synthesis with face image based voice control [41.17483247506426]
  Lip-to-Speech (Lip2Speech) synthesis, which predicts corresponding speech from talking face images, has witnessed significant progress with various models and training strategies. We propose a zero-shot personalized Lip2Speech synthesis method, in which face images control speaker identities.
  arXiv  Detail & Related papers  (2023-05-09T02:37:29Z)
• TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation [61.564874831498145]
  TranSpeech is a speech-to-speech translation model with bilateral perturbation. We establish a non-autoregressive S2ST technique, which repeatedly masks and predicts unit choices. TranSpeech shows a significant improvement in inference latency, enabling speedup up to 21.4x than autoregressive technique.
  arXiv  Detail & Related papers  (2022-05-25T06:34:14Z)
• SVTS: Scalable Video-to-Speech Synthesis [105.29009019733803]
  We introduce a scalable video-to-speech framework consisting of two components: a video-to-spectrogram predictor and a pre-trained neural vocoder. We are the first to show intelligible results on the challenging LRS3 dataset.
  arXiv  Detail & Related papers  (2022-05-04T13:34:07Z)
• LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading [24.744371143092614]
  The aim of this work is to investigate the impact of crossmodal self-supervised pre-training for speech reconstruction (video-to-audio) by leveraging the natural co-occurrence of audio and visual streams in videos. We propose LipSound2, which consists of an encoder-decoder architecture and location-aware attention mechanism to map face image sequences to mel-scale spectrograms.
  arXiv  Detail & Related papers  (2021-12-09T08:11:35Z)
• Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition [159.9312272042253]
  Wav-BERT is a cooperative acoustic and linguistic representation learning method. We unify a pre-trained acoustic model (wav2vec 2.0) and a language model (BERT) into an end-to-end trainable framework.
  arXiv  Detail & Related papers  (2021-09-19T16:39:22Z)
• LiRA: Learning Visual Speech Representations from Audio through Self-supervision [53.18768477520411]
  We propose Learning visual speech Representations from Audio via self-supervision (LiRA) Specifically, we train a ResNet+Conformer model to predict acoustic features from unlabelled visual speech. We show that our approach significantly outperforms other self-supervised methods on the Lip Reading in the Wild dataset.
  arXiv  Detail & Related papers  (2021-06-16T23:20:06Z)
• SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding [61.02342238771685]
  Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
  arXiv  Detail & Related papers  (2020-10-05T19:29:49Z)

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.