On the use of spectral peak parameters in voice conversion

International audienceThis paper addresses the problem of low transformed data variance, or "over-smoothing," in spectral transformation for Voice Conversion. In examining a classic GMM-based transformation with cepstral coefficients, we show that this problem lies, not only in the transformation model (as commonly assumed), but also in the choice of spectral parameterization. Consequently, we propose an alternative method for spectral transformation using spectral peaks and an HMM with Gaussian state distributions. The spectral peaks are shown to offer higher inter-speaker feature correlation and yield higher transformed data variance than their cepstral coefficient counterparts. Additionally, the accuracy of the transformed envelopes is examined

On transforming spectral peaks in voice conversion

International audienceThis paper explores the benefits of transforming spectral peaks in voice conversion. First, in examining classic GMMbased transformation with cepstral coefficients, we show that the lack of transformed data variance ("over-smoothing") can be related to the choice of spectral parameterization. Consequently, we propose an alternative parameterization using spectral peaks. The peaks are transformed using HMMs with Gaussian state distributions. Two learning variants and post-processing treating peak evolution in time are also examined. In comparing the different transformation approaches, spectral peaks are shown to offer higher interspeaker feature correlation and yield higher transformed data variance than their cepstral coefficient counterparts

Efficient Gaussian Mixture Model Evaluation in Voice Conversion

Abstract Voice conversion refers to the adaptation of the characteristics of a source speaker's voice to those of a target speaker. Gaussian mixture models (GMM) have been found to be efficient in the voice conversion task. The GMM parameters are estimated from a training set with the goal to minimize the mean squared error (MSE) between the transformed and target vectors. Obviously, the quality of the GMM model plays an important role in achieving better voice conversion quality. This paper presents a very efficient approach for the evaluation of GMM models directly from the model parameters without using any test data, facilitating the improvement of the transformation performance especially in the case of embedded implementations. Though the proposed approach can be used in any application that utilizes GMM based transformation, we take voice conversion as an example application throughout the paper. The proposed approach is experimented with in this context and evaluated against an MSE based evaluation method. The results show that the proposed method is in line with all subjective observations and MSE results

Real-time Voice Adaptation with Abstract Normalization and Sound-indexed Based Search

This paper proposes a two-step system to conduct real-time voice adaptation in the field of speech processing. The first step includes recording and pre-processing to form a voice profile. Secondly is real-time input of the voice and adapting the input into a target voice. Concerning the fact that individual voices’ structure are habitually varying, this paper suggests a method for converting them into a comparable format. The new method is called abstract normalization which cuts the voice data into smaller sounds. From the sounds are generated an abstracted, simplified version of the data using a level of abstraction along with parameter fitting. The normalized data is used to generate a sound-index which consists of a sequence hash that represents the current object in a simpler fashion. The indices are used to compare different sounds/voices for adaptation. This effectively transforms the speech-related challenges into a search problem rather than a biometric one. To assess the approach, voice profile data are compared against each other as a method to verify the sound-index. Lastly a real-time voice input using alternating levels of abstraction is run against a voice profile created with Norwegian words. The degree of adaptation success is measured in percentage, and experimental results show that while accuracy is not yet excellent, the concept was validated

Cross-Lingual Voice Conversion with Non-Parallel Data

In this project a Phonetic Posteriorgram (PPG) based Voice Conversion system is implemented. The main goal is to perform and evaluate conversions of singing voice. The cross-gender and cross-lingual scenarios are considered. Additionally, the use of spectral envelope based MFCC and pseudo-singing dataset for ASR training are proposed in order to improve the performance of the system in the singing context

Discussion On Effective Restoration Of Oral Speech Using Voice Conversion Techniques Based On Gaussian Mixture Modeling

Today\u27s world consists of many ways to communicate information. One of the most effective ways to communicate is through the use of speech. Unfortunately many lose the ability to converse. This in turn leads to a large negative psychological impact. In addition, skills such as lecturing and singing must now be restored via other methods. The usage of text-to-speech synthesis has been a popular resolution of restoring the capability to use oral speech. Text to speech synthesizers convert text into speech. Although text to speech systems are useful, they only allow for few default voice selections that do not represent that of the user. In order to achieve total restoration, voice conversion must be introduced. Voice conversion is a method that adjusts a source voice to sound like a target voice. Voice conversion consists of a training and converting process. The training process is conducted by composing a speech corpus to be spoken by both source and target voice. The speech corpus should encompass a variety of speech sounds. Once training is finished, the conversion function is employed to transform the source voice into the target voice. Effectively, voice conversion allows for a speaker to sound like any other person. Therefore, voice conversion can be applied to alter the voice output of a text to speech system to produce the target voice. The thesis investigates how one approach, specifically the usage of voice conversion using Gaussian mixture modeling, can be applied to alter the voice output of a text to speech synthesis system. Researchers found that acceptable results can be obtained from using these methods. Although voice conversion and text to speech synthesis are effective in restoring voice, a sample of the speaker before voice loss must be used during the training process. Therefore it is vital that voice samples are made to combat voice loss

Conversão do contorno de pitch por divisão de componentes para aplicação em sistemas de conversão de voz

Dissertação [mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2009Esta dissertação propõe uma nova técnica de conversão do contorno de pitch para aplicação em sistemas de conversão de voz. O principal objetivo deste trabalho é possibilitar a aplicação do método proposto aos mais diferentes tipos de sistemas de conversão de voz sem que para tanto seja necessário adaptar ou criar um novo banco de sinais de fala. A abordagem proposta considera o algoritmo MOMEL (modelling melody) para dividir o contorno de pitch levando em conta os componentes macroprosódico e microprosódico, sendo que cada um deles é convertido separadamente. A contribuição do componente macroprosódico, obtida pela interpolação dos dados usando a codificação INTSINT (international transcription system for intonation), é então convertida utilizando um modelo de misturas gaussianas (GMM); enquanto, a contribuição do componente microprosódico é convertida por seleção de segmentos de contorno de pitch. Os problemas inerentes à avaliação de desempenho dos sistemas de conversão de voz são discutidos e um parâmetro denominado índice de desempenho é modificado para permitir uma avaliação objetiva da conversão do contorno de pitch. O desempenho do método proposto é confrontado com dois dos métodos mais utilizados na literatura: conversão utilizando normalização gaussiana (GN) e GMM. O desempenho dos diferentes métodos considerados são avaliados através de dois testes subjetivos: de preferência e de similaridade. Os resultados obtidos ratificam a medida adotada, indicando uma preferência pelo método proposto através da melhoria significativa de desempenho frente aos demais métodos avaliados. A flexibilidade da nova abordagem possibilita ampla gama de aplicações nos mais variados tipos de sistemas de conversão de voz

Efficient Approaches for Voice Change and Voice Conversion Systems

In this thesis, the study and design of Voice Change and Voice Conversion systems are presented. Particularly, a voice change system manipulates a speaker’s voice to be perceived as it is not spoken by this speaker; and voice conversion system modifies a speaker’s voice, such that it is perceived as being spoken by a target speaker. This thesis mainly includes two sub-parts. The first part is to develop a low latency and low complexity voice change system (i.e. includes frequency/pitch scale modification and formant scale modification algorithms), which can be executed on the smartphones in 2012 with very limited computational capability. Although some low-complexity voice change algorithms have been proposed and studied, the real-time implementations are very rare. According to the experimental results, the proposed voice change system achieves the same quality as the baseline approach but requires much less computational complexity and satisfies the requirement of real-time. Moreover, the proposed system has been implemented in C language and was released as a commercial software application. The second part of this thesis is to investigate a novel low-complexity voice conversion system (i.e. from a source speaker A to a target speaker B) that improves the perceptual quality and identity without introducing large processing latencies. The proposed scheme directly manipulates the spectrum using an effective and physically motivated method – Continuous Frequency Warping and Magnitude Scaling (CFWMS) to guarantee high perceptual naturalness and quality. In addition, a trajectory limitation strategy is proposed to prevent the frame-by-frame discontinuity to further enhance the speech quality. The experimental results show that the proposed method outperforms the conventional baseline solutions in terms of either objective tests or subjective tests

Voice source characterization for prosodic and spectral manipulation

The objective of this dissertation is to study and develop techniques to decompose the speech signal into its two main components: voice source and vocal tract. Our main efforts are on the glottal pulse analysis and characterization. We want to explore the utility of this model in different areas of speech processing: speech synthesis, voice conversion or emotion detection among others. Thus, we will study different techniques for prosodic and spectral manipulation. One of our requirements is that the methods should be robust enough to work with the large databases typical of speech synthesis. We use a speech production model in which the glottal flow produced by the vibrating vocal folds goes through the vocal (and nasal) tract cavities and its radiated by the lips. Removing the effect of the vocal tract from the speech signal to obtain the glottal pulse is known as inverse filtering. We use a parametric model fo the glottal pulse directly in the source-filter decomposition phase. In order to validate the accuracy of the parametrization algorithm, we designed a synthetic corpus using LF glottal parameters reported in the literature, complemented with our own results from the vowel database. The results show that our method gives satisfactory results in a wide range of glottal configurations and at different levels of SNR. Our method using the whitened residual compared favorably to this reference, achieving high quality ratings (Good-Excellent). Our full parametrized system scored lower than the other two ranking in third place, but still higher than the acceptance threshold (Fair-Good). Next we proposed two methods for prosody modification, one for each of the residual representations explained above. The first method used our full parametrization system and frame interpolation to perform the desired changes in pitch and duration. The second method used resampling on the residual waveform and a frame selection technique to generate a new sequence of frames to be synthesized. The results showed that both methods are rated similarly (Fair-Good) and that more work is needed in order to achieve quality levels similar to the reference methods. As part of this dissertation, we have studied the application of our models in three different areas: voice conversion, voice quality analysis and emotion recognition. We have included our speech production model in a reference voice conversion system, to evaluate the impact of our parametrization in this task. The results showed that the evaluators preferred our method over the original one, rating it with a higher score in the MOS scale. To study the voice quality, we recorded a small database consisting of isolated, sustained Spanish vowels in four different phonations (modal, rough, creaky and falsetto) and were later also used in our study of voice quality. Comparing the results with those reported in the literature, we found them to generally agree with previous findings. Some differences existed, but they could be attributed to the difficulties in comparing voice qualities produced by different speakers. At the same time we conducted experiments in the field of voice quality identification, with very good results. We have also evaluated the performance of an automatic emotion classifier based on GMM using glottal measures. For each emotion, we have trained an specific model using different features, comparing our parametrization to a baseline system using spectral and prosodic characteristics. The results of the test were very satisfactory, showing a relative error reduction of more than 20% with respect to the baseline system. The accuracy of the different emotions detection was also high, improving the results of previously reported works using the same database. Overall, we can conclude that the glottal source parameters extracted using our algorithm have a positive impact in the field of automatic emotion classification