@inproceedings{FreisingerSeebergerRanzenbergeretal.2025, author = {Freisinger, Steffen and Seeberger, Philipp and Ranzenberger, Thomas and Bocklet, Tobias and Riedhammer, Korbinian}, title = {Towards Multi-Level Transcript Segmentation: LoRA Fine-Tuning for Table-of-Contents Generation}, series = {Interspeech 2025}, booktitle = {Interspeech 2025}, publisher = {ISCA}, address = {ISCA}, issn = {2958-1796}, doi = {10.21437/Interspeech.2025-2792}, pages = {276 -- 280}, year = {2025}, abstract = {Segmenting speech transcripts into thematic sections benefits both downstream processing and users who depend on written text for accessibility. We introduce a novel approach to hierarchical topic segmentation in transcripts, generating multi-level tables of contents that capture both topic and subtopic boundaries. We compare zero-shot prompting and LoRA fine-tuning on large language models, while also exploring the integration of high-level speech pause features. Evaluations on English meeting recordings and multilingual lecture transcripts (Portuguese, German) show significant improvements over established topic segmentation baselines. Additionally, we adapt a common evaluation measure for multi-level segmentation, taking into account all hierarchical levels within one metric.}, language = {en} } @article{LeeBokLeeWagneretal.2025, author = {Lee, Seanie and Bok Lee, Dong and Wagner, Dominik and Kang, Minki and Seong, Haebin and Bocklet, Tobias and Lee, Juho and Hwang, Sung Ju}, title = {SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models,}, series = {Findings of the Association for Computational Linguistics: ACL 2025}, journal = {Findings of the Association for Computational Linguistics: ACL 2025}, publisher = {Association for Computational Linguistics}, address = {Vienna, Austria}, pages = {2053-2069}, year = {2025}, abstract = {Deploying large language models (LLMs) in real-world applications requires robust safety guard models to detect and block harmful user prompts. While large safety guard models achieve strong performance, their computational cost is substantial. To mitigate this, smaller distilled models are used, but they often underperform on "hard" examples where the larger model provides accurate predictions. We observe that many inputs can be reliablyhandled by the smaller model, while only a small fraction require the larger model's capacity. Motivated by this, we propose SafeRoute, a binary router that distinguishes hard examples from easy ones. Our method selectively applies the larger safety guard model to the data that the router considers hard, improving efficiency while maintaining accuracy compared to solely using the larger safety guard model. Experimental results on multiple benchmark datasets demonstrate that our adaptive model selection significantly enhances the trade-off between computational cost and safety performance, outperforming relevant baselines.}, language = {en} } @inproceedings{WagnerBaumannEngertetal.2025, author = {Wagner, Dominik and Baumann, Ilja and Engert, Natalie and Lee, Seanie and N{\"o}th, Elmar and Riedhammer, Korbinian and Bocklet, Tobias}, title = {Personalized Fine-Tuning with Controllable Synthetic Speech from LLM-Generated Transcripts for Dysarthric Speech Recognition}, series = {Interspeech 2025}, booktitle = {Interspeech 2025}, publisher = {ISCA}, address = {ISCA}, issn = {2958-1796}, doi = {10.21437/Interspeech.2025-2155}, pages = {3294 -- 3298}, year = {2025}, abstract = {In this work, we present our submission to the Speech Accessibility Project challenge for dysarthric speech recognition. We integrate parameter-efficient fine-tuning with latent audio representations to improve an encoder-decoder ASR system. Synthetic training data is generated by fine-tuning Parler-TTS to mimic dysarthric speech, using LLM-generated prompts for corpus-consistent target transcripts. Personalization with x-vectors consistently reduces word error rates (WERs) over non-personalized fine-tuning. AdaLoRA adapters outperform full fine-tuning and standard low-rank adaptation, achieving relative WER reductions of ∼23\% and ∼22\%, respectively. Further improvements (∼5\% WER reduction) come from incorporating wav2vec 2.0-based audio representations. Training with synthetic dysarthric speech yields up to ∼7\% relative WER improvement over personalized fine-tuning alone.}, language = {en} } @inproceedings{BaumannWagnerRiedhammeretal.2025, author = {Baumann, Ilja and Wagner, Dominik and Riedhammer, Korbinian and Bocklet, Tobias}, title = {Pathology-Aware Speech Encoding and Data Augmentation for Dysarthric Speech Recognition}, series = {Interspeech 2025}, booktitle = {Interspeech 2025}, publisher = {ISCA}, address = {ISCA}, issn = {2958-1796}, doi = {10.21437/Interspeech.2025-2724}, pages = {3289 -- 3293}, year = {2025}, abstract = {Automatic speech recognition (ASR) for pathologic speech remains a major challenge due to high variability in articulation, phonation, and prosody distortions. In this work, we propose a pathology-aware speech encoder based on BEST-RQ pre-training, which incorporates 46k hours of speech, including pathologic and atypical speech. We continue pre-training for domain adaptation and experiment with etiology-specific codebooks. We achieve a 13.2\% relative word error rate (WER) improvement using the pathology-aware speech encoder with etiology-specific continued pre-training. Additionally, we examine the impact of incorporating synthetic and out-of-domain (OOD) data to further enhance ASR performance. Synthetic data reduces WER by up to 8.7\%, while OOD data improves WER by 12.2\%. Finally, we introduce a semantic similaritybased data augmentation technique to optimize data selection, achieving a WER improvement of up to 9.7\% while minimizing the need for additional training data.}, language = {en} } @inproceedings{WagnerBaumannBocklet2025, author = {Wagner, Dominik and Baumann, Ilja and Bocklet, Tobias}, title = {Vocoder-Free Non-parallel Conversion of Whispered Speech With Masked Cycle-Consistent Generative Adversarial Networks}, publisher = {Springer}, address = {Cham}, isbn = {978-3-032-02548-7}, doi = {10.1007/978-3-032-02548-7_20}, pages = {235-246}, year = {2025}, abstract = {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).}, language = {en} } @article{RanzenbergerBaumannBayerletal.2025, author = {Ranzenberger, Thomas and Baumann, Ilja and Bayerl, Sebastian and Wagner, Dominik and Bocklet, Tobias and Riedhammer, Korbinian}, title = {Evaluation of recognition errors of hybrid and transformer-based ASR systems in German video lectures}, series = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2025 - Book}, journal = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2025 - Book}, publisher = {ESSV 2025}, address = {Halle, Deutschland}, pages = {101-108}, year = {2025}, abstract = {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.}, language = {en} }