Combining Residual Networks with LSTMs for Lipreading (1703.04105v4)

Abstract: We propose an end-to-end deep learning architecture for word-level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks. We train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging database of 500-size target-words consisting of 1.28sec video excerpts from BBC TV broadcasts. The proposed network attains word accuracy equal to 83.0, yielding 6.8 absolute improvement over the current state-of-the-art, without using information about word boundaries during training or testing.

• The paper's main contribution is an end-to-end architecture integrating 3D convolution, ResNet, and Bi-LSTM for improved lipreading.
• The study reports an 83% top-1 accuracy on the challenging LRW dataset, a 6.8% improvement over prior methods.
• The approach highlights the benefit of merging spatiotemporal feature extraction with sequential modeling for robust visual speech recognition.

Overview of "Combining Residual Networks with LSTMs for Lipreading"

The paper "Combining Residual Networks with LSTMs for Lipreading" presents a novel end-to-end deep learning architecture designed for the task of word-level visual speech recognition, often referred to as lipreading. This research falls within the intersection of speech recognition and computer vision, leveraging recent advances in deep learning to address the challenges inherent to visual speech recognition.

Architecture Composition

The proposed architecture integrates three distinct types of neural networks into a cohesive system:

1. Spatiotemporal Convolutional Network: Serving as the front-end, this component captures the short-term dynamics in the mouth regions using 3D convolutions, which are instrumental in processing the spatiotemporal information inherent in videos.
2. Residual Network (ResNet): Applied to each time step, the ResNet is employed to extract robust features. The paper uses a 34-layer version that enhances the system's ability to manage deep layers without degradation, evidenced by a performance boost compared to standard DNNs.
3. Bidirectional LSTM (Bi-LSTM): For the back-end, a two-layer Bi-LSTM network is deployed. This component effectively captures the sequence dependencies both forward and backward in time, which is critical for the sequential nature of speech signals.

The system is trained and evaluated on the Lipreading In-The-Wild (LRW) database, which is characterized by its complexity arising from speaker and pose variability, as well as utterances extracted from unconstrained, real-world video material like TV broadcasts.

Experimental Results

The architecture achieves significant improvements in word accuracy, reporting an 83.0% top-1 accuracy on the LRW dataset. This marks an absolute improvement of 6.8% over the state-of-the-art at the time of the paper, highlighting the effectiveness of the multi-component approach.

Several configurations are tested to underline the contribution of each architectural component. In particular, 3D convolution provides a 5.0% accuracy improvement over 2D convolution, underscoring the value of temporal information in visual speech tasks. Furthermore, replacing temporal convolutions with Bi-LSTM layers enhances recognition accuracy by 3.8%.

Implications and Future Directions

The paper’s findings can guide the development of more advanced visual speech recognition systems, offering potential applications in environments where audio-based recognition could struggle, such as noisy public spaces. Moreover, such systems could augment silent communication devices and contribute to biometric authentication solutions.

Future work could explore the expansion of this framework to sentence-level recognition tasks, potentially incorporating external LLMs to handle larger vocabularies and more complex linguistic structures. Additionally, integrating this architecture with audio-visual systems could lead to more robust multi-modal speech recognition applications.

The paper provides an important step toward understanding and improving lipreading systems, illustrating the power of combining residual networks with recurrent models in processing visual sequences. As visual speech recognition continues to evolve, research efforts like this will be instrumental in pushing the boundaries of what is currently achievable in the field.