A conversão de voz é um problema emergente em processamento de fala e voz com um crescente interesse comercial, tanto em aplicações como Tradução Fala para Fala (Speech-to-Speech Translation - SST) e em sistemas Text-To-Speech (TTS) personalizados. Um sistema de Conversão de Voz deve permitir o mapeamento de características acústicas de sentenças pronunciadas por um falante origem para valores correspondentes da voz do falante destino, de modo que a saída processada é percebida como uma sentença pronunciada pelo falante destino. Nas últimas duas décadas, o número de contribuições cientícas relacionadas ao problema de conversão de voz tem crescido consideravelmente, e um panorama sólido do processo histórico, assim como de técnicas propostas são indispensáveis para contribuição neste campo. O objetivo deste trabalho é realizar um levantamento geral das técnicas utilizadas para resolver o problema, apontando vantagens e desvantagens de cada método, e a partir deste estudo, desenvolver novas ferramentas. Dentre as contribuições do trabalho, foram desenvolvidos um método para decomposição espectral em termos de bases radiais, mapas fonéticos articiais, agrupamentos k-verossímeis, funções de empenamento em frequência entre outras, com o intuito de implementar um sistema de conversão de voz inter-linguístico independente de texto de alta qualidade. / 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 scientic 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 eld. The goal of this work is to provide a critical survey that combines historical presentation to technical discussion while pointing out advantages and drawbacks of each technique, and from this study, to develop new tools. Some contributions proposed in this work include a method for spectral decomposition in terms of radial basis functions, articial phonetic map, warping functions among others, in order to implement a text-independent crosslingual voice conversion system of high quality.

A conversão de voz é um problema emergente em processamento de fala e voz com um crescente interesse comercial, tanto em aplicações como Tradução Fala para Fala (Speech-to-Speech Translation - SST) e em sistemas Text-To-Speech (TTS) personalizados. Um sistema de Conversão de Voz deve permitir o mapeamento de características acústicas de sentenças pronunciadas por um falante origem para valores correspondentes da voz do falante destino, de modo que a saída processada é percebida como uma sentença pronunciada pelo falante destino. Nas últimas duas décadas, o número de contribuições cientícas relacionadas ao problema de conversão de voz tem crescido consideravelmente, e um panorama sólido do processo histórico, assim como de técnicas propostas são indispensáveis para contribuição neste campo. O objetivo deste trabalho é realizar um levantamento geral das técnicas utilizadas para resolver o problema, apontando vantagens e desvantagens de cada método, e a partir deste estudo, desenvolver novas ferramentas. Dentre as contribuições do trabalho, foram desenvolvidos um método para decomposição espectral em termos de bases radiais, mapas fonéticos articiais, agrupamentos k-verossímeis, funções de empenamento em frequência entre outras, com o intuito de implementar um sistema de conversão de voz inter-linguístico independente de texto de alta qualidade. / 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 scientic 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 eld. The goal of this work is to provide a critical survey that combines historical presentation to technical discussion while pointing out advantages and drawbacks of each technique, and from this study, to develop new tools. Some contributions proposed in this work include a method for spectral decomposition in terms of radial basis functions, articial phonetic map, warping functions among others, in order to implement a text-independent crosslingual voice conversion system of high quality.

Speech synthesis is a technology that increasingly influences our daily lives, in the form of smart assistants, advanced translation systems and similar applications. In this thesis, the phenomenon of making one’s voice sound like the voice of someone else is explored. This topic is called voice conversion and needs to be done without altering the linguistic content of speech. More specifically, a Cycle-Consistent Adversarial Network that has proven to work well in a monolingual setting, is evaluated in a multilingual environment. The model is trained to convert voices between native speakers from the Nordic countries. In the experiments no parallel, transcribed or aligned speech data is being used, forcing the model to focus on the raw audio signal. The goal of the thesis is to evaluate if performance is degraded in a multilingual environment, in comparison to monolingual voice conversion, and to measure the impact of the potential performance drop. In the study, performance is measured in terms of naturalness and speaker similarity between the generated speech and the target voice. For evaluation, listening tests are conducted, as well as objective comparisons of the synthesized speech. The results show that voice conversion between a Swedish and Norwegian speaker is possible and also that it can be performed without performance degradation in comparison to Swedish-to-Swedish conversion. Furthermore, conversion between Finnish and Swedish speakers, as well as Danish and Swedish speakers show a performance drop for the generated speech. However, despite the performance decrease, the model produces fluent and clearly articulated converted speech in all experiments. These results are noteworthy, especially since the network is trained on less than 15 minutes of nonparallel speaker data for each speaker. This thesis opens up for further areas of research, for instance investigating more languages, more recent Generative Adversarial Network architectures and devoting more resources to tweaking the hyperparameters to further optimize the model for multilingual voice conversion. / Talsyntes är ett område som allt mer influerar vår vardag, exempelvis genom smarta assistenter, avancerade översättningssystem och liknande användningsområden. I det här examensarbetet utforskas fenomenet röstkonvertering, som innebär att man får en talare att låta som någon annan, utan att det som sades förändras. Mer specifikt undersöks ett Cycle-Consistent Adversarial Network som fungerat väl för röstkonvertering inom ett enskilt språk för röstkonvertering mellan olika språk. Det neurala nätverket tränas för konvertering mellan röster från olika modersmålstalare från de nordiska länderna. I experimenten används ingen parallell eller transkriberad data, vilket tvingar modellen att endast använda sig av ljudsignalen. Målet med examensarbetet är att utvärdera om modellens prestanda försämras i en flerspråkig kontext, jämfört med en enkelspråkig sådan, samt mäta hur stor försämringen i sådant fall är. I studien mäts prestanda i termer av kvalitet och talarlikhet för det genererade talet och rösten som efterliknas. För att utvärdera detta genomförs lyssningstester, samt objektiva analyser av det genererade talet. Resultaten visar att röstkonvertering mellan en svensk och norsk talare är möjlig utan att modellens prestanda försämras, jämfört med konvertering mellan svenska talare. För konvertering mellan finska och svenska talare, samt danska och svenska talare försämrades däremot kvaliteten av det genererade talet. Trots denna försämring producerade modellen tydligt och sammanhängande tal i samtliga experiment. Det här är anmärkningsvärt eftersom modellen tränades på mindre än 15 minuter icke-parallel data för varje talare. Detta examensarbete öppnar upp för nya framtida studier, exempelvis skulle fler språk kunna inkluderas eller nyare varianter av typen Generative Adversarial Network utvärderas. Mer resurser skulle även kunna läggas på att optimera hyperparametrarna för att ytterligare optimera den undersökta modellen för flerspråkig röstkonvertering.