Audio-visual video-to-speech synthesis with synthesized input audio
The paper credibly shows that incorporating synthesized audio as an auxiliary input in a second-stage audiovisual synthesis model improves video-to-speech reconstruction quality and intelligibility in benchmarks, though gains depend on model variant and dataset.
Reading guidance
- Verdict
- full-text draft · priority high · confidence high
- Why it matters
- Demonstrates a novel two-stage audiovisual speech synthesis pipeline that leverages synthesized audio as an intermediate representation, trained with modality dropout, achieving improved objective metrics and enabling robust audiovisual speech reconstruction from silent videos.
- What to trust
- Basis: full text. Coverage: high. 8 evidence records back the review.
- What is weak
- Effectiveness depends on the quality of synthesized audio from the first-stage V2A model; WER improvements are inconsistent across datasets and model variants; no demonstrated results for in-the-wild noisy conditions or real-time inference. Objective metrics (PESQ, STOI, ESTOI, WER) on benchmark datasets; lack of in-the-wild, noisy environment, or real-time latency evaluations; WER unavailable for TCD-TIMIT due to lack of suitable pre-trained ASR models. Benchmark evaluations only; relies on pretrained base models; no real-time or latency evaluation; no in-the-wild or noisy environment testing; dependency on quality of synthesized audio inputs. Applies to video-to-audio reconstruction tasks in benchmark datasets with limited speaker diversity and controlled conditions; does not address noisy or real-time conditions. Overclaim risk: medium.
- Read before
- SSI review rubric
- Read next
- SSI archive
Axes
- Task
- speech-reconstruction
- Modality
- Silent video plus synthesized audio
- Hardware
- camera
- Body site
- lip
- Output
- speech-audio
- Vocabulary
- Sentence-level benchmark vocabularies
- Metrics
- GRID (4 speakers, seen): PESQ 1.95, STOI 0.698, ESTOI 0.532, WER 3.67%; GRID (33 speakers, seen): PESQ 2.10, STOI 0.723, ESTOI 0.553, WER 2.65%; TCD-TIMIT (3 lipspeakers, seen): PESQ 1.44, STOI 0.566, ESTOI 0.411; LRW unseen: WER down to 24.96% in best raw waveform models; metrics reported include PESQ, STOI, ESTOI, WER.
- Evaluation mode
- Multi-dataset quantitative benchmark comparison with multiple training variants and modalities in raw waveform and mel-spectrogram domains, including seen and unseen speaker splits.
- Review confidence
- high
- Overclaim risk
- medium
Expert take
This paper presents a significant advance in video-to-speech synthesis by proposing and evaluating a two-stage approach that treats synthesized audio as an essential intermediate modality to enhance speech reconstruction from silent video. The AV2A architectures append a learnable audio encoder to a pretrained V2A model and are trained with modality dropout to prevent reliance on a single modality. Across benchmark datasets (GRID with 4 and 33 speakers, both seen and unseen, TCD-TIMIT lipspeakers, and LRW), AV2A models, particularly raw waveform V2A-WaveGAN variants with audio pretraining and modality dropout using ground-truth audio during training, consistently improve perceptual and intelligibility metrics (PESQ up to 2.10, STOI up to 0.723, ESTOI up to 0.553), with reductions in WER compared to base V2A models. However, improvements are not universal: some mel-spectrogram models underperform older baselines on certain datasets, and WER gains are modest or fluctuating, especially on unseen speaker splits. The work’s main contribution lies in demonstrating the feasibility and benefits of incorporating synthesized audio in a staged AV2A approach, with novel training methods to balance modalities. This overcomes traditional practices discarding audio at inference, framing synthesized audio as a useful intermediate representation. Nonetheless, limitations remain due to lack of real-time testing, reliance on benchmark datasets under clean conditions, and dependence on the quality of first-stage synthesized audio. The approach is promising for silent-video speech reconstruction research but requires further development and deployment-oriented evaluation for practical use.
True value
Demonstrates a novel two-stage audiovisual speech synthesis pipeline that leverages synthesized audio as an intermediate representation, trained with modality dropout, achieving improved objective metrics and enabling robust audiovisual speech reconstruction from silent videos.
What changed
Canon before
Most video-to-speech synthesis systems used either video only or included audio during training but discarded audio input at inference, treating missing audio as unavailable rather than as an explicit intermediate representation.
Delta from canon
Introduces a two-stage pipeline leveraging synthesized audio from a first-stage V2A model as explicit input to a second-stage AV2A model, trained with modality dropout to robustly combine video and audio modalities for speech reconstruction.
Position in field
A strong contribution to video-to-speech synthesis literature centered on staged audiovisual reconstruction incorporating synthesized audio.
Evidence
“ In this work we investigate video-to-speech synthesis mod- Lipreading, i.e., predicting text from a silent video, has also els, following an encoder-decoder structure, that include audio been investigated in situations where the audio modality is and video inputs during both training and inference. ”
author_claim · Abstract · confidence 1.00
“ In this work we investigate the effect of using in the task of video-to-speech synthesis (V2A), involving video and audio inputs for video-to-speech synthesis during both the reconstruction of the speech signal from a silent video. training and inference. ”
actual_novelty · I. INTRODUCTION · confidence 1.00
“ We construct the audio-visual-to-audio Generator by ap- pending an audio encoder to the video-to-audio Generator, which receives the synthesized raw waveforms as input and C. ”
actual_novelty · III. RAW WAVEFORM MODELS · confidence 0.95
“ Datasets [18], [67]; (2) a seen speaker setting with 33 speakers, origi- nally proposed in [12] and (3) an unseen speaker setting with We conduct experiments on three audio-visual face and 33 speakers used in [11]–[13], [67]. speech datasets which are widely used in the video-to-speech literature: GRID [25], TCD-TIMIT [26] and LRW [27]. ”
validation_scope · V. EXPERIMENTAL METHODOLOGY · confidence 1.00
“ In our experiments AV2A-MelSpec-S 2.00 0.717 0.535 2.72 + modality dropout 2.04 0.715 0.536 2.82 with GRID (4 speakers, seen), AV2A-WaveGAN with audio + modality dropout (GT audio) 2.02 0.720 0.539 2.53 pre-training and modality dropout (with ground truth audio) V2A-MelSpec-S with audio pre-training [67] 2.01 0.719 0.536 3.66 outperforms all other raw waveform models across all metrics. ”
metric · VI. RESULTS · confidence 1.00
“ AV2A-MelSpec-S with audio pre-training and modality dropout outperforms all In experiments with GRID (33 speakers, seen), shown other works across reconstruction metrics; however, SVTS-S in Table IV, we observe that AV2A-WaveGAN with audio achieves a lower WER. pre-training and modality dropout (GT audio) outperforms With the TCD-TIMIT (3 lipspeakers, seen) split, AV2A- all other comparable methods across reconstruction metrics. ”
metric · VI. RESULTS · confidence 1.00
“ AV2A-MelSpec-VS with audio pre-training improves TABLE V R ESULTS ON TCD-TIMIT (3 LIPSPEAKERS , SEEN ) upon its corresponding base V2A model in most metrics, and achieves the lowest WER among all comparable methods Method PESQ↑ STOI↑ ESTOI↑ when trained with modality dropout (GT audio). ”
metric · VI. RESULTS · confidence 1.00
“ AV2A-MelSpec-VS with audio pre-training improves TABLE V R ESULTS ON TCD-TIMIT (3 LIPSPEAKERS , SEEN ) upon its corresponding base V2A model in most metrics, and achieves the lowest WER among all comparable methods Method PESQ↑ STOI↑ ESTOI↑ when trained with modality dropout (GT audio). ”
limitation · V. EXPERIMENTAL METHODOLOGY · confidence 1.00
Limits
Technical limits
Effectiveness depends on the quality of synthesized audio from the first-stage V2A model; WER improvements are inconsistent across datasets and model variants; no demonstrated results for in-the-wild noisy conditions or real-time inference.
Evaluation limits
Objective metrics (PESQ, STOI, ESTOI, WER) on benchmark datasets; lack of in-the-wild, noisy environment, or real-time latency evaluations; WER unavailable for TCD-TIMIT due to lack of suitable pre-trained ASR models.
Deployment limits
Benchmark evaluations only; relies on pretrained base models; no real-time or latency evaluation; no in-the-wild or noisy environment testing; dependency on quality of synthesized audio inputs.
Scope limits
Applies to video-to-audio reconstruction tasks in benchmark datasets with limited speaker diversity and controlled conditions; does not address noisy or real-time conditions.