TY - CHAP A1 - Wagner, Dominik A1 - Baumann, Ilja A1 - Bocklet, Tobias T1 - Vocoder-Free Non-parallel Conversion of Whispered Speech With Masked Cycle-Consistent Generative Adversarial Networks N2 - Cycle-consistent generative adversarial networks have been widely used in non-parallel voice conversion (VC). Their ability to learn mappings between source and target features without relying on parallel training data eliminates the need for temporal alignments. However, most methods decouple the conversion of acoustic features from synthesizing the audio signal by using separate models for conversion and waveform synthesis. This work unifies conversion and synthesis into a single model, thereby eliminating the need for a separate vocoder. By leveraging cycle-consistent training and a self-supervised auxiliary training task, our model is able to efficiently generate converted high-quality raw audio waveforms. Subjective listening tests showed that our unified approach achieved improvements of up to 6.7% relative to the baseline in whispered VC. Mean opinion score predictions also yielded stable results in conventional VC (between 0.5% and 2.4% relative improvement). KW - voice conversion KW - generative adversarial networks KW - cycle-consistency KW - masking KW - whispered speech Y1 - 2025 SN - 978-3-032-02548-7 U6 - https://doi.org/10.1007/978-3-032-02548-7_20 PB - Springer CY - Cham ER - TY - JOUR A1 - Ranzenberger, Thomas A1 - Baumann, Ilja A1 - Bayerl, Sebastian A1 - Wagner, Dominik A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Evaluation of recognition errors of hybrid and transformer-based ASR systems in German video lectures JF - Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2025 - Book N2 - We analyze different errors in speech recognition systems, focusing on consecutive insertions and deletions, known as hallucinations and elisions in transformer-based end-to-end automatic speech recognition (ASR) systems. We compare errors from a TDNN-HMM, and whisper-based models on English and German spontaneous speech. Based on a human annotated subset of German lecture videos, we investigate whether these blocks of deletions affect the semantics of the utterance. Whisper performs best and preserves the meaning in 90% of the annotated error segments even containing consecutive deletions on this subset. We analyze the word error rate and do further analysis of errors using natural language processing to detect lemmatization errors, compound word errors, and out-of-vocabulary words. We discuss possible reasons and mitigations. Y1 - 2025 UR - https://www.essv.de/pdf/2025_101_108.pdf PB - ESSV 2025 CY - Halle, Deutschland ER -