Leveraging audio-visual speech effectively via deep learning

The rising popularity of neural networks, combined with the recent proliferation of online audio-visual media, has led to a revolution in the way machines encode, recognize, and generate acoustic and visual speech. Despite the ubiquity of naturally paired audio-visual data, only a limited number of works have applied recent advances in deep learning to leverage the duality between audio and video within this domain. This thesis considers the use of neural networks to learn from large unlabelled datasets of audio-visual speech to enable new practical applications. We begin by training a visual speech encoder that predicts latent features extracted from the corresponding audio on a large unlabelled audio-visual corpus. We apply the trained visual encoder to improve performance on lip reading in real-world scenarios. Following this, we extend the idea of video learning from audio by training a model to synthesize raw speech directly from raw video, without the need for text transcriptions. Remarkably, we find that this framework is capable of reconstructing intelligible audio from videos of new, previously unseen speakers. We also experiment with a separate speech reconstruction framework, which leverages recent advances in sequence modeling and spectrogram inversion to improve the realism of the generated speech. We then apply our research in video-to-speech synthesis to advance the state-of-the-art in audio-visual speech enhancement, by proposing a new vocoder-based model that performs particularly well under extremely noisy scenarios. Lastly, we aim to fully realize the potential of paired audio-visual data by proposing two novel frameworks that leverage acoustic and visual speech to train two encoders that learn from each other simultaneously. We leverage these pre-trained encoders for deepfake detection, speech recognition, and lip reading, and find that they consistently yield improvements over training from scratch.Open Acces

Articulatory-WaveNet: Deep Autoregressive Model for Acoustic-to-Articulatory Inversion

Acoustic-to-Articulatory Inversion, the estimation of articulatory kinematics from speech, is an important problem which has received significant attention in recent years. Estimated articulatory movements from such models can be used for many applications, including speech synthesis, automatic speech recognition, and facial kinematics for talking-head animation devices. Knowledge about the position of the articulators can also be extremely useful in speech therapy systems and Computer-Aided Language Learning (CALL) and Computer-Aided Pronunciation Training (CAPT) systems for second language learners. Acoustic-to-Articulatory Inversion is a challenging problem due to the complexity of articulation patterns and significant inter-speaker differences. This is even more challenging when applied to non-native speakers without any kinematic training data. This dissertation attempts to address these problems through the development of up-graded architectures for Articulatory Inversion. The proposed Articulatory-WaveNet architecture is based on a dilated causal convolutional layer structure that improves the Acoustic-to-Articulatory Inversion estimated results for both speaker-dependent and speaker-independent scenarios. The system has been evaluated on the ElectroMagnetic Articulography corpus of Mandarin Accented English (EMA-MAE) corpus, consisting of 39 speakers including both native English speakers and Mandarin accented English speakers. Results show that Articulatory-WaveNet improves the performance of the speaker-dependent and speaker-independent Acoustic-to-Articulatory Inversion systems significantly compared to the previously reported results