Voice conversion versus speaker verification: an overview

A speaker verification system automatically accepts or rejects a claimed identity of a speaker based on a speech sample. Recently, a major progress was made in speaker verification which leads to mass market adoption, such as in smartphone and in online commerce for user authentication. A major concern when deploying speaker verification technology is whether a system is robust against spoofing attacks. Speaker verification studies provided us a good insight into speaker characterization, which has contributed to the progress of voice conversion technology. Unfortunately, voice conversion has become one of the most easily accessible techniques to carry out spoofing attacks; therefore, presents a threat to speaker verification systems. In this paper, we will briefly introduce the fundamentals of voice conversion and speaker verification technologies. We then give an overview of recent spoofing attack studies under different conditions with a focus on voice conversion spoofing attack. We will also discuss anti-spoofing attack measures for speaker verification.Published versio

Statistical parametric speech synthesis for Ibibio

Ibibio is a Nigerian tone language, spoken in the south-east coastal region of Nigeria. Like most African languages, it is resource-limited. This presents a major challenge to conventional approaches to speech synthesis, which typically require the training of numerous predictive models of linguistic features such as the phoneme sequence (i.e., a pronunciation dictionary plus a letter-to-sound model) and prosodic structure (e.g., a phrase break predictor). This training is invariably supervised, requiring a corpus of training data labelled with the linguistic feature to be predicted. In this paper, we investigate what can be achieved in the absence of many of these expensive resources, and also with a limited amount of speech recordings. We employ a statistical parametric method, because this has been found to offer good performance even on small corpora, and because it is able to directly learn the relationship between acoustics and whatever linguistic features are available, potentially mitigating the absence of explicit representations of intermediate linguistic layers such as prosody. We present an evaluation that compares systems that have access to varying degrees of linguistic structure. The simplest system only uses phonetic context (quinphones), and this is compared to systems with access to a richer set of context features, with or without tone marking. It is found that the use of tone marking contributes significantly to the quality of synthetic speech. Future work should therefore address the problem of tone assignment using a dictionary and the building of a prediction module for out-of-vocabulary words. Key words: speech synthesis, Ibibio, low-resource languages, HT

Prosody and Wavelets: Towards a natural speaking style conversion

Speech is the basis of human communication: in everyday life we automatically decode speech into language regardless of who speaks. In a similar way, we have the ability to recognize di erent speakers, despite the linguistic content of the speech. Additionally to the voice individuality of the speaker, the particular prosody of speech involves relevant information concerning the identity, age, social group or economical status of the speaker, helping us identify the person to whom we are talking without seeing the speaker. Voice conversion systems deal with the conversion of a speech signal to sound as if it was uttered by another speaker. It has been an important amount of work in the conversion of the timber of the voice, the spectral features, meanwhile the conversion of pitch and the way it temporarily evolves, modeling the speaker dependent prosody, is mostly achieved by just controlling the level and range. This thesis focuses on prosody conversion, proposing an approach based on a wavelet transformation of the pitch contours. It has been performed a study of the wavelet domain, discerning among the di erent timing of the prosodic events, thus allowing an improved modeling of them. Consequently, the prosody conversion is achieved in the wavelet domain, using regression techniques originally developed for the spectral features conversion, in voice conversion systems