LP Conformer for VSR/AVSR

LP Conformer stands for “Linear Projection Conformer” which is a visual speech recognition (VSR) model that is able to read lip movements and transform them into text.This model was proposed by Google in 2023 and published in this paper: “Conformers Are All You Need For Visual Speech Recognition”. An illustration of the model can be seen in the following figure:

As shown in the previous figure, this model consists of three main components:

  • Visual Front-End:
    This part is responsible of convert the sequence of input video frames into a sequence of features. In the paper, they tried different architectures for that:

    • VGG: Since the VGG network is originally introduced for image classification which takes 2D inputs, they had to adapt it to 3D inputs by decomposing the 3D convolutions into separate 2D spatial convolutions for all layers and 1D temporal convolutions at the last layer.

    • Conv3D: They used the same VGG network. However, they replaced the 2D convolutions with 3D convolutions.

    • ViT: They used Vision transformers (ViT) which, similar to VGG, were originally introduced for image classification. They used $6$-layer ViT with 3D patches.

    • LP: They used the embarrassingly simple linear layer. To further speed up this computation, they down-sampled the video frames from $128 \times 128 \times 3$ image to $64 \times 64 \times 3$ for VSR, and $32 \times 32 \times 3$ for AVSR which makes the linear projection $12288 \times 512$ and $3072 \times 512$ respectively.

  • Encoder:
    The encoder processes the visual features produced by the Visual Front-End (VFE). In the paper, they have tried two different architectures:

    • Transformer: The Transformer was originally introduced for machine translation. For VSR, it is applied on the visual features produced by the front-end. For our audio-visual

    • Conformer: Conformers are convolution-augmented transformers that were initially introduced for audio-only speech recognition.

  • Decoder:
    A decoder was used only for the visual speech recognition (VSR) task. They used a RNN-T architecture with a $9$-layer LSTM with cell size $2048$ and embedding dimension $128$.


To train all variants of the model, audio-visual datasets were used. The visual data was pre-processed by running the MediaPipe face detector to extract $128 \times 128$ RGB mouth tracks. The audio data was pre-processed by extracting 80-dimensional log-mel filter-bank coefficients extracted from a $25ms$ Hann window with a stride of $10ms$; every three frames are stacked to yield a $240$-dimensional frame every $30ms$. The text transcripts are tokenized at the character level. In the paper, they used the following three audio-visual datasets:

  • TED-LRS3:
    TED-LRS3 contains over $400$ hours of TED and TEDx videos in segments of $6$ seconds, covering more than $5,000$ different speakers.

  • YT:
    They mined public YouTube videos for audio-visual speech with high confidence transcripts by matching the user-uploaded captions with the result of a pre-trained audio-only speech recognition system. Then, an audio-visual synchronization classifier is used to filter out cases where the found face is a still image or contains a dubbed voice. This dataset contains $100k$ hours of transcribed video segments that are up to $15$ seconds long.

  • MEET360:
    MEET360 is a simulated audio-visual dataset that was collected internally. Volunteers were asked to conduct 20-minute meetings in a conference room, and recorded by a 360-degree GoPro camera. The resultant spherical video is then projected to a hi-res 2D video, and transcribed by human annotators. The 2D video is segmented by the ground-truth non-speaking regions to yield clips averaging $25$ seconds, totaling $11$ hours of video. Unlike TED-LRS3 and YT where a single video track is associated with each audio track, there are multiple video tracks associated with each audio track in MEET360.

The first two datasets (LRS3 and YT) were used with visual speech recognition task, while the last dataset (MEET360) were used with audio-visual speech diarization.

Experiments & Results

For experiments with visual-only inputs, each frame was down-sampled to $64 \times 64$, and they used $16$-layer full-context Conformer encoder with model dimension $1024$. For experiments with audio-visual inputs, each frame is down-sampled to $32 \times 32$, then processed by two Conformer layers before being concatenated to the acoustic features. The concatenated features are then passed to a $15$-layer full-context Conformer encoder with model dimension $512$. For all experiments, no external LM was used.


To test the model on visual speech recognition task, they pre-trained the model on YT dataset, and fine-tuned it on TED-LRS3. For the decoder, They used a RNN-T architecture with a $9$-layer LSTM with cell size $2048$ and embedding dimension $128$. The model was trained for $300k$ steps using a global batch size of $16$ and the Adam optimizer with a $3k$-step warmup and a peak learning rate of $5e^{- 4}$ that is cosine-annealed to $5e^{- 5}$. Results reported in the following table are obtained using beam size of $8$ where VO-WER stands for “Visual-Only Word Error Rate” and AV-WER stands for “Audio-Visual Word Error Rate”:

From the previous table, we can see that LP Conformer significantly outperforms prior work, achieving $12.8\%$ WER, which is a $25\%$ improvement over the previous state-of-the-art ViT Conformer at $17.0\%$ WER.

Given huge amount of training data, this result convincingly demonstrates that sophisticated visual front-ends like residual networks, 3D convolutional neural networks, or vision transformers are not necessary for visual speech recognition. Again, only given huge amount of training data.

Audio-Visual Diarization

Audio-visual diarization is the task where the model determines who is the speaker in a given utterance and when did he/she spoke by using the audio-visual information available. To measure diarization performance, they used two metrics:

  • Diarization Error Rate (DER): which is the percentage duration of audio that was mislabeled over the total duration according to the following formula; where “false alarm” is the duration that were mislabeled as speech, “miss detection” is the duration of speech that were missed, and “confusion” is the duration that were assigned to the wrong speaker.
\[DER = \frac{false\ alarm + missed\ detection + confusion}{total}\]
  • Word Diarization Error Rate (WDER): which is the percentage of words that are mislabeled with the wrong speaker according to the following formula; where $C_{IS}$, $S_{IS}$, and $I_{IS}$ are the correct, substituted, and inserted words with incorrect speaker labeling, and $C + S + I$ is the total number of hypothesis words.
\[WDER = \frac{C_{IS} + S_{IS} + I_{IS}}{C + S + I}\]

The following table compares the LP Conformer against the VGG Transformer (VGG front-end with a Transformer encoder) and the VGG Conformer (VGG front-end with a Conformer encoder). They found that the LP Conformer performed the best for both speech recognition and diarization. Also, LP Conformer was trained about $20\%$ faster than the VGG Conformer, while using less memory.

Robustness to Missing Video

Robustness to missing video is an essential feature for audio-visual speech recognition (AVSR) systems, because the visual modality can often be missing. To test out robustness to missing video, they used TED-LRS3 for evaluation after adding $10dB$ noise to the audio. They tried different setups:

  • Audio Baseline: audio-only model, no visual modality were used.

  • Drop utterance: each video was dropped following Bernoulli distribution.

  • Drop Frame: each frame was dropped following Bernoulli distribution.

  • Drop Start: contiguous frames dropped from the start.

  • Drop Middle: contiguous frames dropped from the middle.

  • Drop End: contiguous frames dropped from the end.

All models were pre-trained on YT dataset and fine-tuned on TED-LRS3 dataset. Results show that LP Conformer is robust to all types of missing video which is illustrated in the following figure where we can see that performance deteriorates as the percentage of frames dropped increases, but is never worse than that of the equivalent audio-only model.