"Notic My Speech" -- Blending Speech Patterns With Multimedia

Speech as a natural signal is composed of three parts - visemes (visual part of speech), phonemes (spoken part of speech), and language (the imposed structure). However, video as a medium for the delivery of speech and a multimedia construct has mostly ignored the cognitive aspects of speech delivery. For example, video applications like transcoding and compression have till now ignored the fact how speech is delivered and heard. To close the gap between speech understanding and multimedia video applications, in this paper, we show the initial experiments by modelling the perception on visual speech and showing its use case on video compression. On the other hand, in the visual speech recognition domain, existing studies have mostly modeled it as a classification problem, while ignoring the correlations between views, phonemes, visemes, and speech perception. This results in solutions which are further away from how human perception works. To bridge this gap, we propose a view-temporal attention mechanism to model both the view dependence and the visemic importance in speech recognition and understanding. We conduct experiments on three public visual speech recognition datasets. The experimental results show that our proposed method outperformed the existing work by 4.99% in terms of the viseme error rate. Moreover, we show that there is a strong correlation between our model's understanding of multi-view speech and the human perception. This characteristic benefits downstream applications such as video compression and streaming where a significant number of less important frames can be compressed or eliminated while being able to maximally preserve human speech understanding with good user experience.

audino: A Modern Annotation Tool for Audio and Speech

In this paper, we introduce a collaborative and modern annotation tool for audio and speech: audino. The tool allows annotators to define and describe temporal segmentation in audios. These segments can be labelled and transcribed easily using a dynamically generated form. An admin can centrally control user roles and project assignment through the admin dashboard. The dashboard also enables describing labels and their values. The annotations can easily be exported in JSON format for further processing. The tool allows audio data to be uploaded and assigned to a user through a key-based API. The flexibility available in the annotation tool enables annotation for Speech Scoring, Voice Activity Detection (VAD), Speaker Diarisation, Speaker Identification, Speech Recognition, Emotion Recognition tasks and more. The MIT open source license allows it to be used for academic and commercial projects.

C3VQG: Category Consistent Cyclic Visual Question Generation

Visual Question Generation (VQG) is the task of generating natural questions based on an image. Popular methods in the past have explored image-to-sequence architectures trained with maximum likelihood which have demonstrated meaningful generated questions given an image and its associated ground-truth answer. VQG becomes more challenging if the image contains rich context information describing its different semantic categories. In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers. Our approach solves two major shortcomings of existing VQG systems: (i) minimize the level of supervision and (ii) replace generic questions with category relevant generations. Most importantly, through eliminating expensive answer annotations, the required supervision is weakened. Using different categories enables us to exploit different concepts as the inference requires only the image and category. Mutual information is maximized between the image, question, and answer category in the latent space of our VAE. A novel category consistent cyclic loss is proposed to enable the model to generate consistent predictions with respect to the answer category, reducing its redundancies and irregularities. Additionally, we also impose supplementary constraints on the latent space of our generative model to provide structure based on categories and enhance generalization by encapsulating decorrelated features within each dimension. Through extensive experiments, the proposed C3VQG outperforms the state-of-the-art visual question generation methods with weak supervision.

COBRA: Contrastive Bi-Modal Representation Algorithm

There are a wide range of applications that involve multi-modal data, such as cross-modal retrieval, visual question-answering, and image captioning. Such applications are primarily dependent on aligned distributions of the different constituent modalities. Existing approaches generate latent embeddings for each modality in a joint fashion by representing them in a common manifold. However these joint embedding spaces fail to sufficiently reduce the modality gap, which affects the performance in downstream tasks. We hypothesize that these embeddings retain the intra-class relationships but are unable to preserve the inter-class dynamics. In this paper, we present a novel framework COBRA that aims to train two modalities (image and text) in a joint fashion inspired by the Contrastive Predictive Coding (CPC) and Noise Contrastive Estimation (NCE) paradigms which preserve both inter and intra-class relationships. We empirically show that this framework reduces the modality gap significantly and generates a robust and task agnostic joint-embedding space. We outperform existing work on four diverse downstream tasks spanning across seven benchmark cross-modal datasets.

End-to-end Named Entity Recognition from English Speech

Named entity recognition (NER) from text has been a widely studied problem and usually extracts semantic information from text. Until now, NER from speech is mostly studied in a two-step pipeline process that includes first applying an automatic speech recognition (ASR) system on an audio sample and then passing the predicted transcript to a NER tagger. In such cases, the error does not propagate from one step to another as both the tasks are not optimized in an end-to-end (E2E) fashion. Recent studies confirm that integrated approaches (e.g., E2E ASR) outperform sequential ones (e.g., phoneme based ASR). In this paper, we introduce a first publicly available NER annotated dataset for English speech and present an E2E approach, which jointly optimizes the ASR and NER tagger components. Experimental results show that the proposed E2E approach outperforms the classical two-step approach. We also discuss how NER from speech can be used to handle out of vocabulary (OOV) words in an ASR system.

Multi-modal Automated Speech Scoring using Attention Fusion

In this study, we propose a novel multi-modal end-to-end neural approach for automated assessment of non-native English speakers' spontaneous speech using attention fusion. The pipeline employs Bi-directional Recurrent Convolutional Neural Networks and Bi-directional Long Short-Term Memory Neural Networks to encode acoustic and lexical cues from spectrograms and transcriptions, respectively. Attention fusion is performed on these learned predictive features to learn complex interactions between different modalities before final scoring. We compare our model with strong baselines and find combined attention to both lexical and acoustic cues significantly improves the overall performance of the system. Further, we present a qualitative and quantitative analysis of our model.

An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms

Twitter is a social media platform where users express opinions over a variety of issues. Posts offering grievances or complaints can be utilized by private/ public organizations to improve their service and promptly gauge a low-cost assessment. In this paper, we propose an iterative methodology which aims to identify complaint based posts pertaining to the transport domain. We perform comprehensive evaluations along with releasing a novel dataset for the research purposes.