The way characters move and behave in computer and video games are important factors in their believability, which has an impact on the player’s experience. This project explores Imitation Learning using limited amounts of data as an approach to creating human-like AI behaviour in games, and through a user study investigates what factors determine if a character is human-like, when observed through the characters first-person perspective. The idea is to create or shape AI behaviour by recording one's own actions. The implemented framework uses a Nearest Neighbour algorithm with a KD-tree as the policy which maps a state to an action. Results showed that the chosen approach was able to create human-like AI behaviour while respecting the performance constraints of a modern 3D game. / Sättet karaktärer rör sig och beter sig på i dator- och tvspel är viktiga faktoreri deras trovärdighet, som i sin tur har en inverkan på spelarens upplevelse. Det här projektet utforskar Imitation Learning med begränsad mängd data som etttillvägagångssätt för att skapa människolik rörelse för AI-karaktärer i spel, ochutforskar genom en användarstudie vilka faktorer som avgör om en karaktärär människolik, när karaktären observeras genom dess förstapersonsperspektiv. Iden är att skapa eller forma AI-beteende genom att spela in sina egna handlingar. Det implementerade ramverket använder en Nearest Neighbour-algoritmmed ett KD-tree som den policy som kopplar ett tillstånd till en handling. Resultatenvisade att det valda tillvägagångssättet lyckades skapa människolikt AI-beteende samtidigt som det respekterar beräkningskomplexitetsrestriktionersom ett modernt 3D-spel har.

Articulatory copy synthesis (ACS), a subarea of speech inversion, refers to the reproduction of natural utterances and involves both the physiological articulatory processes and their corresponding acoustic results. This thesis proposes two novel methods for the ACS of human speech using the articulatory speech synthesizer VocalTractLab (VTL) to address or mitigate the existing problems of speech inversion, such as non-unique mapping, acoustic variation among different speakers, and the time-consuming nature of the process. The first method involved finding appropriate VTL gestural scores for given natural utterances using a genetic algorithm. It consisted of two steps: gestural score initialization and optimization. In the first step, gestural scores were initialized using the given acoustic signals with speech recognition, grapheme-to-phoneme (G2P), and a VTL rule-based method for converting phoneme sequences to gestural scores. In the second step, the initial gestural scores were optimized by a genetic algorithm via an analysis-by-synthesis (ABS) procedure that sought to minimize the cosine distance between the acoustic features of the synthetic and natural utterances. The articulatory parameters were also regularized during the optimization process to restrict them to reasonable values. The second method was based on long short-term memory (LSTM) and convolutional neural networks, which were responsible for capturing the temporal dependence and the spatial structure of the acoustic features, respectively. The neural network regression models were trained, which used acoustic features as inputs and produced articulatory trajectories as outputs. In addition, to cover as much of the articulatory and acoustic space as possible, the training samples were augmented by manipulating the phonation type, speaking effort, and the vocal tract length of the synthetic utterances. Furthermore, two regularization methods were proposed: one based on the smoothness loss of articulatory trajectories and another based on the acoustic loss between original and predicted acoustic features. The best-performing genetic algorithms and convolutional LSTM systems (evaluated in terms of the difference between the estimated and reference VTL articulatory parameters) obtained average correlation coefficients of 0.985 and 0.983 for speaker-dependent utterances, respectively, and their reproduced speech achieved recognition accuracies of 86.25% and 64.69% for speaker-independent utterances of German words, respectively. When applied to German sentence utterances, as well as English and Mandarin Chinese word utterances, the neural network based ACS systems achieved recognition accuracies of 73.88%, 52.92%, and 52.41%, respectively. The results showed that both of these methods not only reproduced the articulatory processes but also reproduced the acoustic signals of reference utterances. Moreover, the regularization methods led to more physiologically plausible articulatory processes and made the estimated articulatory trajectories be more articulatorily preferred by VTL, thus reproducing more natural and intelligible speech. This study also found that the convolutional layers, when used in conjunction with batch normalization layers, automatically learned more distinctive features from log power spectrograms. Furthermore, the neural network based ACS systems trained using German data could be generalized to the utterances of other languages.