Wavelet-based voice morphing

This paper presents a new multi-scale voice morphing algorithm. This algorithm enables a user to transform one person's speech pattern into another person's pattern with distinct characteristics, giving it a new identity, while preserving the original content. The voice morphing algorithm performs the morphing at different subbands by using the theory of wavelets and models the spectral conversion using the theory of Radial Basis Function Neural Networks. The results obtained on the TIMIT speech database demonstrate effective transformation of the speaker identity

Estimation of glottal closure instants in voiced speech using the DYPSA algorithm

Published versio

Research methods and intelligibility studies

This paper first briefly reviews the concept of intelligibility as it has been employed in both English as a Lingua Franca (ELF) and world Englishes (WE) research. It then examines the findings of the Lingua Franca Core (LFC), a list of phonological features that empirical research has shown to be important for safeguarding mutual intelligibility between non-native speakers of English. The main point of the paper is to analyse these findings and demonstrate that many of them can be explained if three perspectives (linguistic, psycholinguistic and historical-variationist) are taken. This demonstration aims to increase the explanatory power of the concept of intelligibility by providing some theoretical background. An implication for ELF research is that at the phonological level, internationally intelligible speakers have a large number of features in common, regardless of whether they are non-native speakers or native speakers. An implication for WE research is that taking a variety-based, rather than a features-based, view of phonological variation and its connection with intelligibility is likely to be unhelpful, as intelligibility depends to some extent on the phonological features of individual speakers, rather than on the varieties per se

Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer

A good supervised embedding for a specific machine learning task is only sensitive to changes in the label of interest and is invariant to other confounding factors. We leverage the concept of repeatability from measurement theory to describe this property and propose to use the intra-class correlation coefficient (ICC) to evaluate the repeatability of embeddings. We then propose a novel regularizer, the ICC regularizer, as a complementary component for contrastive losses to guide deep neural networks to produce embeddings with higher repeatability. We use simulated data to explain why the ICC regularizer works better on minimizing the intra-class variance than the contrastive loss alone. We implement the ICC regularizer and apply it to three speech tasks: speaker verification, voice style conversion, and a clinical application for detecting dysphonic voice. The experimental results demonstrate that adding an ICC regularizer can improve the repeatability of learned embeddings compared to only using the contrastive loss; further, these embeddings lead to improved performance in these downstream tasks.Comment: Accepted by NeurIPS 202