The purpose of a voice conversion (VC) system is to change the perceived speaker identity of a speech signal. In this paper, we propose a new algorithm based on converting the LPC spectrum and predicting the residual as a function of the target envelope parameters. We conduct listening tests based on speaker discrimination of same/difference pairs to measure the accuracy by which the converted voices match the desired target voices. To establish the level of human performance as a baseline, we first measure the ability of listeners to discriminate between original speech utterances under three conditions: normal, fundamental frequency and duration normalized, and LPC coded. Additionally, the spectral parameter conversion function is tested in isolation by listening to source, target, and converted speakers as LPC coded speech. The results show that the speaker identity of speech whose LPC spectrum has been converted can be recognized as the target speaker with the same level of perform...

A new voice conversion method that improves the quality of the voice conversion output at higher sampling rates is proposed. Speaker Transformation Algorithm Using Segmental Codebooks (STASC) is modified to process source and target speech spectra in different subbands. The new method ensures better conversion at sampling rates above 16KHz. Discrete Wavelet Transform (DWT) is employed for subband decomposition to estimate the speech spectrum better with higher resolution. Faster voice conversion is achieved since the computational complexity decreases at a lower sampling rate. A Voice Conversion System (VCS) is implemented using the proposed algorithm with necessary tools. The performance of the proposed method is demonstrated by both subjective listening tests and applications to film dubbing and looping. In ABX listening tests, the listeners preferred the subband based output by 92.1% as compared to the full-band based output.

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A new approach to speech morphing is presented which avoids the extraction of fundamental and formant frequencies as well as the detection of phone or syllable boundaries. All prominent spectral and temporal features of the source and target utterances are automatically related and interpolated. The method consists of three main parts: LPC-based source-filter decomposition, separate interpolation, and composition of the morphed speech signal. The paper focuses on the alignment and interpolation problems on three speech signal layers: the timing structure on a phone- and syllable-level, the shape of the frequency spectrum including formants and other spectral properties, and the micro-timing of the source signal. Particularly, the source signal alignment and interpolation is described since it is most crucial for the resulting quality of the modified speech signal. The new morphing procedure was applied to utterances taken from the freely available CMU ARCTIC speech corpus and assessed by a perceptual MOS experiment. Preliminary

Most of the existing voice conversion methods calculate the optimal transformation function from a given set of paired acoustic vectors of the source and target speakers. The alignment of the phonetically equivalent source and target frames is problematic when the training corpus available is not parallel, although this is the most realistic situation. The alignment task is even more difficult in cross-lingual applications because the phoneme sets may be different in the involved languages. In this paper, a new iterative alignment method based on acoustic distances is proposed. The method is shown to be suitable for text-independent and cross-lingual voice conversion, and the conversion scores obtained in our evaluation experiments are not far from the performance achieved by using parallel training corpora. Index Terms: speech synthesis, cross-lingual voice conversion, alignment, GMM, weighted frequency warping

In this paper we address the problem of Voice Morphing. We attempt to transform the spectral characteristics of a source speakers speech signal so that the listener would believe that the speech was uttered by a target speaker. The voice morphing system transforms the spectral envelope as represented by a Linear Prediction model. The transformation is achieved by codebook mapping using the Generative Topographic Mapping, a non-linear, latent variable, parametrically constrained, Gaussian Mixture Model. Keywords: Voice Morphing, LPC, GTM, codebook mapping. 1.

Voice conversion is an emergent problem in voice and speech processing with increasing commercial interest, due to applications such as Speech-to-Speech Translation (SST) and personalized Text-To-Speech (TTS) systems. A Voice Conversion system should allow the mapping of acoustical features of sentences pronounced by a source speaker to values corresponding to the voice of a target speaker, in such a way that the processed output is perceived as a sentence uttered by the target speaker. In the last two decades the number of scientific contributions to the voice conversion problem has grown considerably, and a solid overview of the historical process as well as of the proposed techniques is indispensable for those willing to contribute to the field. The goal of this text is to provide a critical survey that combines historical presentation to technical discussion while pointing out advantages and drawbacks of each technique, and to bring a discussion of future directions, specially referring to the development of a perceptual benchmark process for voice conversion systems. 1.

The growing interest in emotional speech synthesis urges effective emotion conversion techniques to be explored. This paper estimates the relevance of three speech components (spectral envelope, residual excitation and prosody) for synthesizing identifiable emotional speech, in order to be able to customize voice conversion techniques to the specific characteristics of each emotion. The analysis has been based on a listening test with a set of synthetic mixed-emotion utterances that draw their speech components from emotional and neutral recordings. Results prove the importance of transforming residual excitation for the identification of emotions that are not fully conveyed through prosodic means (such as cold anger or sadness in our Spanish corpus).

The paper describes the Blizzard Challenge 2009 participation of MARY TTS, an open-source TTS system using a unit selection voice. We briefly outline the new language support framework we provide so that people can add support for their languages to MARY TTS, and describe how that framework was used for building a Mandarin Chinese system and voice. The system performs well for English and reasonably for Chinese.

This paper presents a comparison of methods for transforming voice quality in neutral synthetic speech to match cheerful, aggressive, and depressed expressive styles. Neutral speech is generated using the unit selection system in the MARY TTS platform and a large neutral database in German. The output is modified using voice conversion techniques to match the target expressive styles, the focus being on spectral envelope conversion for transforming the overall voice quality. Various improvements over the state-of-the-art weighted codebook mapping and GMM based voice conversion frameworks are employed resulting in three algorithms. Objective evaluation results show that all three methods result in comparable reduction in objective distance to target expressive TTS outputs whereas weighted frame mapping and GMM based transformations were perceived slightly better than the weighted codebook mapping outputs in generating the target expressive style in a listening test. Index Terms: voice quality transformation, voice conversion, emotional speech synthesis 1.