This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.

WaveNet: A Generative Model for Raw Audio

https://psycnet.apa.org/journals/rev/74/6/431.pdf

Perception of the speech code.

This paper introduces WaveNet, a deep neural network for generating raw audio
waveforms. The model is fully probabilistic and autoregressive, with the
predictive distribution for each audio sample conditioned on all previous ones;
nonetheless we show that it can be efficiently trained on data with tens of
thousands of samples per second of audio. When applied to text-to-speech, it
yields state-of-the-art performance, with human listeners rating it as
significantly more natural sounding than the best parametric and concatenative
systems for both English and Mandarin. A single WaveNet can capture the
characteristics of many different speakers with equal fidelity, and can switch
between them by conditioning on the speaker identity. When trained to model
music, we find that it generates novel and often highly realistic musical
fragments. We also show that it can be employed as a discriminative model,
returning promising results for phoneme recognition.

Simple structure and other common principles of factor rotation do not in general provide strong grounds for attributing explanatory significance to the factors which they select. In contrast, it is shown that an extension of Cattell's principle of rotation to Proportional Profiles (PP) offers a basis for determining explanatory factors for three-way or higher order multi-mode data. Conceptual models are developed for two basic patterns of multi-mode data variation, systemand object-variation, and PP analysis is found to apply in the system-variation case. Although PP was originally formulated as a principle of rotation to be used with classic two-way factor analysis, it is shown to embody a latent three-mode factor model, which is here made explicit and generalized frown two to N "parallel occasions". As originally formulated, PP rotation was restricted to orthogonal factors. The generalized PP model is demonstrated to give unique "correct" solutions with oblique, non-simple structure, and even non-linear factor structures. A series of tests, conducted with synthetic data of known factor composition, demonstrate the capabilities of linear and non-linear versions of the model, provide data on the minimal necessary conditions of uniqueness, and reveal the properties of the analysis procedures when these minimal conditions are not fulfilled. In addition, a mathematical proof is presented for the uniqueness of the solution given certain conditions on the data. Three-mode PP factor analysis is applied to a three-way set of real data consisting of the fundamental and first three formant frequencies of 11 persons saying 8 vowels. A unique solution is extracted, consisting of three factors which are highly meaningful and consistent with prior knowledge and theory concerning vowel quality. The relationships between the three-mode PP model and Tucker's multi-modal model, McDonald's non-linear model and Carroll and Chang's multi-dimensional scaling model are explored.

/pdf/foundations-of-the-parafac-procedure-models-and-conditions-4hwy0qyl1q.pdf

Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis

A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, is presented and examined. This technique uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum: (1) the critical-band spectral resolution, (2) the equal-loudness curve, and (3) the intensity-loudness power law. The auditory spectrum is then approximated by an autoregressive all-pole model. A 5th-order all-pole model is effective in suppressing speaker-dependent details of the auditory spectrum. In comparison with conventional linear predictive (LP) analysis, PLP analysis is more consistent with human hearing. The effective second formant F2' and the 3.5-Bark spectral-peak integration theories of vowel perception are well accounted for. PLP analysis is computationally efficient and yields a low-dimensional representation of speech. These properties are found to be useful in speaker-independent automatic-speech recognition.

Perceptual linear predictive (PLP) analysis of speech

Acoustic theory of speech production

This is a representative collection of the work of one of the world's leading scholars in the area of speech acoustics. It follows the development over the past 15 years of research presented in the author's previous publications on speech analysis, feature theory, and applications to language descriptions. Most of the articles have had very restricted distribution--many appearing only in the Quarterly Progress Reports issued by Dr. Fant's laboratory. The first part of the book covers manifold aspects of speech analysis such as instrumental techniques, spectrum data, formant statistics with an emphasis on Swedish vowels and stops, speaker dependencies, normalization procedures, production theory, and coarticulation. The second part analyzes established feature systems and suggests revisions and general discussions on the concept of distinctive features, perception, and the applicability of feature theory to automatic speech recognition. Articles in this part of the book are especially valuable because they represent Dr. Fant's work on "inherent" features and their phonetic correlates as it has evolved since his collaboration in 1952 with Roman Jakobson and Morris Halle. Speech Sounds and Features is the fourth volume in the series Current Studies in Linguistics.

Gunnar Fant

Papers

Acoustic theory of speech production

Speech sounds and features

Acoustic Theory of Speech Production: With Calculations Based on X-Ray Studies of Russian Articulations

Auditory analysis and perception of speech

Calculation of true glottal flow and its components