Voice transformation (VT) aims to change one or more aspects of a speech signal while preserving linguistic information. A subset of VT, Voice conversion (VC) specifically aims to change a source speaker’s speech in such a way that the generated output is perceived as a sentence uttered by a target speaker. Despite many years of research, VC systems still exhibit deficiencies in accurately mimi...

This paper explores the topic of voice conversion as explored in a joint project with Percy Liang (EECS, Berkeley). For our purposes, voice conversion is the process of modifying the speech signal of one speaker (source) such that it sounds as though it had been pronounced by a different speaker (target). Following the Source-Filter model of speech production, we begin by assuming that most of ...

Voice conversion (VC) techniques, which modify a speaker’s voice to sound like another’s, present a threat to automatic speaker verification (SV) systems. In this paper, we evaluate the vulnerability of a state-of-the-art SV system against a converted speech spoofing attack. To overcome the spoofing attack, we implement state-of-the-art converted speech detectors based on shortand long-term fea...

Nowadays, voice conversion is a problem which is intensively analyzed by many researchers. A large group of existing voice conversion systems is based on RELP re-synthesis. Within these systems, the speech signal is pitchsynchronously segmented and described with LSF parameters. A transformation function is acquired by employing pairs of equal time-aligned utterances from source and target spea...

and transformation of the vocal tract spectrum and the pitch contour. The first method (selective pre-emphasis) relies on band-pass filtering to perform vocal tract transformation. The second method (segmental pitch contour model) focuses on a more detailed modeling of pitch contours. Both methods are utilized in the design of a voice conversion algorithm based on codebook mapping. We compare t...

This paper explores the topic of voice conversion as explored in a joint project with Percy Liang (EECS, Berkeley). For our purposes, voice conversion is the process of modifying the speech signal of one speaker (source) so that it sounds as thought it had been pronounced by a different speaker (target). By using a Gaussian mixture model (GMM) to model the features of the source speaker, we can...

Voice conversion systems aim to process speech from a source speaker so it would be perceived as spoken by a target speaker. This paper presents a procedure to improve high resolution voice conversion by modifying the algorithm used for residual estimation. The proposed residual estimation algorithm exploits the temporal dependencies between residuals in consecutive speech frames using a hidden...

This paper describes the voice conversion based on the Mixtures of Factor Analyzers (MFA) which can provide an efficient modeling with a limited amount of training data. As a typical spectral conversion method, a mapping algorithm based on the Gaussian Mixture Model (GMM) has been proposed. In this method two kinds of covariance matrix structures are often used : the diagonal and full covarianc...

The performance of voice conversion has been considerably improved through statistical modeling of spectral sequences. However, the converted speech still contains traces of artificial sounds. To alleviate this, it is necessary to statistically model a source sequence as well as a spectral sequence. In this paper, we introduce STRAIGHT mixed excitation to a framework of the voice conversion bas...

This paper presents a duration-embedded Bi-HMM framework for expressive voice conversion. First, Ward’s minimum variance clustering method is used to cluster all the conversion units (sub-syllables) in order to reduce the number of conversion models as well as the size of the required training database. The duration-embedded Bi-HMM trained with the EM algorithm is built for each sub-syllable cl...