Learning weakly supervised multimodal phoneme embeddings

Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate the role of combining different speech modalities, i.e. audio and visual information representing the lips movements, in a weakly supervised way using Siamese networks and lexical same-different side information. In particular, we ask whether one modality can benefit from the other to provide a richer representation for phone recognition in a weakly supervised setting. We introduce mono-task and multi-task methods for merging speech and visual modalities for phone recognition. The mono-task learning consists in applying a Siamese network on the concatenation of the two modalities, while the multi-task learning receives several different combinations of modalities at train time. We show that multi-task learning enhances discriminability for visual and multimodal inputs while minimally impacting auditory inputs. Furthermore, we present a qualitative analysis of the obtained phone embeddings, and show that cross-modal visual input can improve the discriminability of phonological features which are visually discernable (rounding, open/close, labial place of articulation), resulting in representations that are closer to abstract linguistic features than those based on audio only

Encoding of phonology in a recurrent neural model of grounded speech

We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the MFCC features extracted from the speech signal, and the activations of the layers of the model. Via experiments with phoneme decoding and phoneme discrimination we show that phoneme representations are most salient in the lower layers of the model, where low-level signals are processed at a fine-grained level, although a large amount of phonological information is retain at the top recurrent layer. We further find out that the attention mechanism following the top recurrent layer significantly attenuates encoding of phonology and makes the utterance embeddings much more invariant to synonymy. Moreover, a hierarchical clustering of phoneme representations learned by the network shows an organizational structure of phonemes similar to those proposed in linguistics.Comment: Accepted at CoNLL 201

Understanding video through the lens of language

The increasing abundance of video data online necessitates the development of systems capable of understanding such content. However, building these systems poses significant challenges, including the absence of scalable and robust supervision signals, computational complexity, and multimodal modelling. To address these issues, this thesis explores the role of language as a complementary learning signal for video, drawing inspiration from the success of self-supervised Large Language Models (LLMs) and image-language models. First, joint video-language representations are examined under the text-to-video retrieval task. This includes the study of pre-extracted multimodal features, the influence of contextual information, joint end-to-end learning of both image and video representations, and various frame aggregation methods for long-form videos. In doing so, state-of-the-art performance is achieved across a range of established video-text benchmarks. Second, this work explores the automatic generation of audio description (AD) – narrations describing the visual happenings in a video, for the benefit of visually impaired audiences. An LLM, prompted with multimodal information, including past predictions, and pretrained with partial data sources, is employed for the task. In the process, substantial advancements are achieved in the following areas: efficient speech transcription, long-form visual storytelling, referencing character names, and AD time-point prediction. Finally, audiovisual behaviour recognition is applied to the field of wildlife conservation and ethology. The approach is used to analyse vast video archives of wild primates, revealing insights into individual and group behaviour variations, with the potential for monitoring the effects of human pressures on animal habitats

Multi-Modal Pre-Training for Automated Speech Recognition

Traditionally, research in automated speech recognition has focused on local-first encoding of audio representations to predict the spoken phonemes in an utterance. Unfortunately, approaches relying on such hyper-local information tend to be vulnerable to both local-level corruption (such as audio-frame drops, or loud noises) and global-level noise (such as environmental noise, or background noise) that has not been seen during training. In this work, we introduce a novel approach which leverages a self-supervised learning technique based on masked language modeling to compute a global, multi-modal encoding of the environment in which the utterance occurs. We then use a new deep-fusion framework to integrate this global context into a traditional ASR method, and demonstrate that the resulting method can outperform baseline methods by up to 7% on Librispeech; gains on internal datasets range from 6% (on larger models) to 45% (on smaller models).Comment: Presented at ICASSP 202