This paper investigates the approaches of building WaveNet vocoders with limited training data for voice conversion (VC). Current VC systems using statistical acoustic models always suffer from the quality degradation of converted speech. One of the major causes is the use of hand-crafted vocoders for waveform generation. Recently, with the emergence of WaveNet for waveform modeling, speaker-dependent WaveNet vocoders have been proposed and they can reconstruct speech with better quality than conventional vocoders, such as STRAIGHT. Because training a WaveNet vocoder in the speaker-dependent way requires a relatively large training dataset, it remains a challenge to build a high-quality WaveNet vocoder for VC tasks when the training data of target speakers is limited. In this paper, we propose to build WaveNet vocoders by combining the initialization using a multi-speaker corpus and the adaptation using a small amount of target data, and evaluate this proposed method on the Voice Conversion Challenge (VCC) 2018 dataset which contains approximately 5 minute recordings for each target speaker. Experimental results show that the WaveNet vocoders built using our proposed method outperform conventional STRAIGHT vocoder. Furthermore, our system achieves an average naturalness MOS of 4.13 in VCC 2018, which is the highest among all submitted systems.

Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation. To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and rightto-left (R2L) NMT decoders. This goal is achieved by introducing two Kullback-Leibler divergence regularization terms into the NMT training objective to reduce the mismatch between output probabilities of L2R and R2L models. In addition, we also employ a joint training strategy to allow L2R and R2L models to improve each other in an interactive update process. Experimental results show that our proposed method significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.Input zhīchízhě mēn biǎoshì, zhè liǎngtiáo suìdào jiāng shǐ huánjìng shòuyì bìng bāngzhù jiālìfúníyàzhōu quèbǎo gòngshuǐ gèngjiāānquán.

We introduce MAgent, a platform to support research and development of many-agent reinforcement learning. Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents. Within the interactions among a population of agents, it enables not only the study of learning algorithms for agents' optimal polices, but more importantly, the observation and understanding of individual agent's behaviors and social phenomena emerging from the AI society, including communication languages, leaderships, altruism. MAgent is highly scalable and can host up to one million agents on a single GPU server. MAgent also provides flexible configurations for AI researchers to design their customized environments and agents. In this demo, we present three environments designed on MAgent and show emerged collective intelligence by learning from scratch.

Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently interact with diverse road users in diverse scenarios remains largely unsolved. Learning methods have much to offer towards solving this problem. But they require a realistic multi-agent simulator that generates diverse and competent driving interactions. To meet this need, we develop a dedicated simulation platform called SMARTS (Scalable Multi-Agent RL Training School). SMARTS supports the training, accumulation, and use of diverse behavior models of road users. These are in turn used to create increasingly more realistic and diverse interactions that enable deeper and broader research on multiagent interaction. In this paper, we describe the design goals of SMARTS, explain its basic architecture and its key features, and illustrate its use through concrete multi-agent experiments on interactive scenarios. We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving. Our code is available at https://github.com/huawei-noah/SMARTS.

Abstract. Gradient iterations for the Rayleigh quotient are simple and robust solvers to determine a few of the smallest eigenvalues together with the associated eigenvectors of (generalized) matrix eigenvalue problems for symmetric matrices. Sharp convergence estimates for the Ritz values and Ritz vectors are derived for various steepest descent/ascent gradient iterations. The analysis shows that poorest convergence of the eigenvalue approximations is attained in a three-dimensional invariant subspace; explicit convergence estimates are then derived by means of a mini-dimensional analysis.