Voice transformation refers to a class of techniques that modify the voice characteristics either to conceal the identity or to mimic the voice characteristics of another speaker. Its applications include automatic dialogue replacement and voice generation for people with voice disorders. The diversity in applications makes evaluation of voice transformation a challenging task. The objective of this research is to propose a framework to evaluate intentional voice transformation techniques. Our proposed framework is based on two fundamental qualities: intelligibility and speaker similarity. Intelligibility refers to the clarity of the speech content after voice transformation and speaker similarity measures how well the modified output disguises the source speaker. We measure intelligibility with word error rates and speaker similarity with likelihood of identifying the correct speaker. The novelty of our approach is, we consider whether similarly transformed training data are available to the recognizer. We have demonstrated that this factor plays a significant role in intelligibility and speaker similarity for both human testers and automated recognizers. We thoroughly test two classes of voice transformation techniques: pitch distortion and voice conversion, using our proposed framework. We apply our results for patients with voice hypertension using video self-modeling and preliminary results are presented.

In this dissertation, new approaches in the design of a voice transformation (VT) system for Turkish are proposed. Objectives in this thesis are two-fold. The first objective is to develop standard speech corpora and segmentation tools for Turkish speech research. The second objective is to consider new approaches for VT. A triphone-balanced set of 2462 Turkish sentences is prepared for analysis. An audio corpus of 100 speakers, each uttering 40 sentences out of the 2462-sentence set, is used to train a speech recognition system designed for English. This system is ported to Turkish to obtain a phonetic aligner and a phoneme recognizer. The triphone-balanced sentence set and the phonetic aligner are used to develop a speech corpus for VT. A new voice transformation approach based on Mixed Excitation Linear Prediction (MELP) speech coding framework is proposed. Multi-stage vector quantization of MELP is used to obtain speaker-specific line-spectral frequency (LSF) codebooks for source and target speakers. Histograms mapping the LSF spaces of source and target speakers are used for transformation in the baseline system. The baseline system is improved by a dynamic programming approach to estimate the target LSFs. As a second approach to the VT problem, quantizing the LSFs using k-means clustering algorithm is applied with dimension reduction of LSFs using principle component analysis. This approach provides speaker-specific codebooks out of the speech corpus instead of using MELP&#039 / s pre-trained LSF codebook. Evaluations show that both dimension reduction and dynamic programming improve the transformation performance.

During musical performance and recording, there are a variety of techniques and electronic effects available to transform the singing voice. The particular effect examined in this dissertation is breathiness, where artificial noise is added to a voice to simulate aspiration noise. The typical problem with this effect is that artificial noise does not effectively blend into voices that exhibit high vocal effort. The existing breathy effect does not reduce the perceived effort; breathy voices exhibit low effort. A typical approach to synthesizing breathiness is to separate the voice into a filter representing the vocal tract and a source representing the excitation of the vocal folds. Artificial noise is added to the source to simulate aspiration noise. The modified source is then fed through the vocal tract filter to synthesize a new voice. The resulting voice sounds like the original voice plus noise. Listening experiments were carried out. These listening experiments demonstrated that constant pre-emphasis linear prediction (LP) results in an estimated vocal tract filter that retains the perception of vocal effort. It was hypothesized that reducing the perception of vocal effort in the estimated vocal tract filter may improve the breathy effect. This dissertation presents adaptive pre-emphasis LP (APLP) as a technique to more appropriately model the spectral envelope of the voice. The APLP algorithm results in a more consistent vocal tract filter and an estimated voice source that varies more appropriately with changes in vocal effort. This dissertation describes how APLP estimates a spectral emphasis filter that can transform the spectral envelope of the voice, thereby reducing the perception of vocal effort. A listening experiment was carried out to determine whether APLP is able to transform high effort voices into breathy voices more effectively than constant pre-emphasis LP. The experiment demonstrates that APLP is able to reduce the perceived effort in the voice. In addition, the voices transformed using APLP sound less artificial than the same voices transformed using constant pre-emphasis LP. This indicates that APLP is able to more effectively transform high-effort voices into breathy voices.