Learning Speaker-specific Lip-to-Speech Generation

Varshney, Munender; Yadav, Ravindra Kumar; Namboodiri, Vinay P.; Hegde, Rajesh M.

doi:10.1109/icpr56361.2022.9956600

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…He et al [29] introduced a flow-based non-autoregressive lip-to-speech model named GlowLTS, designed to bypass autoregressive constraints and facilitate faster inference. Varshney et al [30] used framed frame sequences as feature distributions using transformers within an autoencoder context. Their approach yielded marginal improvements, raising the Short-time Objective Intelligibility measure (STOI) measurements from 0.377 to 0.394 and 0.490, respectively, in the Lip2Wav dataset for Chemistry Lectures.…”

Section: Reconstructed Speech With Unconstrained Datasetsmentioning

confidence: 99%

Lip2Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features

Dong,

Xu,

Abel

et al. 2024

Applied Sciences

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In environments characterised by noise or the absence of audio signals, visual cues, notably facial and lip movements, serve as valuable substitutes for missing or corrupted speech signals. In these scenarios, speech reconstruction can potentially generate speech from visual data. Recent advancements in this domain have predominantly relied on end-to-end deep learning models, like Convolutional Neural Networks (CNN) or Generative Adversarial Networks (GAN). However, these models are encumbered by their intricate and opaque architectures, coupled with their lack of speaker independence. Consequently, achieving multi-speaker speech reconstruction without supplementary information is challenging. This research introduces an innovative Gabor-based speech reconstruction system tailored for lightweight and efficient multi-speaker speech restoration. Using our Gabor feature extraction technique, we propose two novel models: GaborCNN2Speech and GaborFea2Speech. These models employ a rapid Gabor feature extraction method to derive lowdimensional mouth region features, encompassing filtered Gabor mouth images and low-dimensional Gabor features as visual inputs. An encoded spectrogram serves as the audio target, and a Long Short-Term Memory (LSTM)-based model is harnessed to generate coherent speech output. Through comprehensive experiments conducted on the GRID corpus, our proposed Gabor-based models have showcased superior performance in sentence and vocabulary reconstruction when compared to traditional end-to-end CNN models. These models stand out for their lightweight design and rapid processing capabilities. Notably, the GaborFea2Speech model presented in this study achieves robust multi-speaker speech reconstruction without necessitating supplementary information, thereby marking a significant milestone in the field of speech reconstruction.

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Section: Reconstructed Speech With Unconstrained Datasetsmentioning

confidence: 99%