Naturalness

About: Naturalness is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 31737 citations.

Lewisian Naturalness and a new Sceptical Challenge

Abstract: The criterion of naturalness represents David Lewis’s attempt to answer some of the sceptical arguments in (meta-) semantics by comparing the naturalness of meaning candidates. Recently, the criterion has been challenged by a new sceptical argument. Williams argues that the criterion cannot rule out the candidates which are not permuted versions of an intended interpretation. He presents such a candidate – the arithmetical interpretation (a specific instantiation of The criterion of naturalness represents David Lewis’s attempt to answer some of the sceptical arguments in (meta-) semantics by comparing the naturalness of meaning candidates. Recently, the criterion has been challenged by a new sceptical argument. Williams argues that the criterion cannot rule out the candidates which are not permuted versions of an intended interpretation. He presents such a candidate – the arithmetical interpretation (a specific instantiation of Henkin’s model), and he argues that it opens up the possibility of Pythagorean worlds, i.e. the worlds similar to ours in which the arithmetical interpretation is the best candidate for a semantic theory. The aim of this paper is a) to reconsider the general conditions for the applicability of Lewis’s criterion of naturalness and b) to show that Williams’s new sceptical challenge is based on a problematic assumption that the arithmetical interpretation is independent of fundamental properties and relations. As I show, if the criterion of naturalness is applied properly, it can respond even to the new sceptical challenge.

Neural HMMS Are All You Need (For High-Quality Attention-Free TTS)

Abstract: Neural sequence-to-sequence TTS has achieved significantly better output quality than statistical speech synthesis using HMMs. However, neural TTS is generally not probabilistic and uses non-monotonic attention. Attention failures increase training time and can make synthesis babble incoherently. This paper describes how the old and new paradigms can be combined to obtain the advantages of both worlds, by replacing attention in neural TTS with an autoregressive left-right no-skip hidden Markov model defined by a neural network. Based on this proposal, we modify Tacotron 2 to obtain an HMM-based neural TTS model with monotonic alignment, trained to maximise the full sequence likelihood without approximation. We also describe how to combine ideas from classical and contemporary TTS for best results. The resulting example system is smaller and simpler than Tacotron 2, and learns to speak with fewer iterations and less data, whilst achieving comparable naturalness prior to the post-net. Our approach also allows easy control over speaking rate.