Portfolio Choice and Liquidity Constraints

This paper generalizes Deaton's (1991) approach to saving under borrowing constraints to incorporate portfolio choice. For infinite horizon, impatient consumers, effects of risk aversion, prudence and temperance on portfolios can be different from those obtained in atemporal models. We confirm the surprising result of portfolio specialization in stocks (Heaton and Lucas, 1997) using a different earnings process, and we provide a rationale for why risk aversion and habit persistence cannot reverse it. We then show that positive correlation of stock returns with permanent, but not transitory, earnings shocks can generate demand for bonds and zero stockholding. However, existing empirical estimates of such correlations are at variance with portfolio data. We offer an alternative explanation of observed stock holding patterns based on fixed stock market entry costs. The entry cost required to keep impatient households out of the stock market is surprisingly small. This suggests that entry costs could generate the observed reluctance of households to undertake stockholding and explain the slowness in the emergence of an "equity culture" among households.Precautionary Saving; Portfolio Choice; Liquidity Constraints; Buffer Stock

Jointly Learning Visual and Auditory Speech Representations from Raw Data

We present RAVEn, a self-supervised multi-modal approach to jointly learn visual and auditory speech representations. Our pre-training objective involves encoding masked inputs, and then predicting contextualised targets generated by slowly-evolving momentum encoders. Driven by the inherent differences between video and audio, our design is asymmetric w.r.t. the two modalities' pretext tasks: Whereas the auditory stream predicts both the visual and auditory targets, the visual one predicts only the auditory targets. We observe strong results in low- and high-resource labelled data settings when fine-tuning the visual and auditory encoders resulting from a single pre-training stage, in which the encoders are jointly trained. Notably, RAVEn surpasses all self-supervised methods on visual speech recognition (VSR) on LRS3, and combining RAVEn with self-training using only 30 hours of labelled data even outperforms a recent semi-supervised method trained on 90,000 hours of non-public data. At the same time, we achieve state-of-the-art results in the LRS3 low-resource setting for auditory speech recognition (as well as for VSR). Our findings point to the viability of learning powerful speech representations entirely from raw video and audio, i.e., without relying on handcrafted features. Code and models will be made public.Comment: 22 page

Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels

Audio-visual speech recognition has received a lot of attention due to its robustness against acoustic noise. Recently, the performance of automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR, respectively) has been substantially improved, mainly due to the use of larger models and training sets. However, accurate labelling of datasets is time-consuming and expensive. Hence, in this work, we investigate the use of automatically-generated transcriptions of unlabelled datasets to increase the training set size. For this purpose, we use publicly-available pre-trained ASR models to automatically transcribe unlabelled datasets such as AVSpeech and VoxCeleb2. Then, we train ASR, VSR and AV-ASR models on the augmented training set, which consists of the LRS2 and LRS3 datasets as well as the additional automatically-transcribed data. We demonstrate that increasing the size of the training set, a recent trend in the literature, leads to reduced WER despite using noisy transcriptions. The proposed model achieves new state-of-the-art performance on AV-ASR on LRS2 and LRS3. In particular, it achieves a WER of 0.9% on LRS3, a relative improvement of 30% over the current state-of-the-art approach, and outperforms methods that have been trained on non-publicly available datasets with 26 times more training data.Comment: Accepted to ICASSP 202

SVTS: scalable video-to-speech synthesis

Video-to-speech synthesis (also known as lip-to-speech) refers to the translation of silent lip movements into the corresponding audio. This task has received an increasing amount of attention due to its self-supervised nature (i.e., can be trained without manual labelling) combined with the ever-growing collection of audio-visual data available online. Despite these strong motivations, contemporary video-to-speech works focus mainly on small- to medium-sized corpora with substantial constraints in both vocabulary and setting. In this work, we introduce a scalable video-to-speech framework consisting of two components: a video-to-spectrogram predictor and a pre-trained neural vocoder, which converts the mel-frequency spectrograms into waveform audio. We achieve state-of-the art results for GRID and considerably outperform previous approaches on LRW. More importantly, by focusing on spectrogram prediction using a simple feedforward model, we can efficiently and effectively scale our method to very large and unconstrained datasets: To the best of our knowledge, we are the first to show intelligible results on the challenging LRS3 dataset

The 10th Santorini conference: Systems medicine, personalised health and therapy. “The odyssey from hope to practice: Patient first. Keep Ithaca always in your mind”, Santorini, Greece, 23–26 May 2022

The full text of this article can be freely accessed on the publisher's website

DataSheet1_The 10th Santorini conference: Systems medicine, personalised health and therapy. “The odyssey from hope to practice: Patient first. Keep Ithaca always in your mind”, Santorini, Greece, 23–26 May 2022.DOCX

The full text of this article can be freely accessed on the publisher's website