7 papers
On the Role of Conversational Timing in Synthetic Training Data for ASR
Máté Gedeon, Péter Mihajlik
Synthetic multi-speaker conversations are widely used to train conversational automatic speech recognition (ASR) systems, but it remains unclear which timing properties make simula…
Efficient ASR Training with Conversations that Never Happened
Máté Gedeon, Péter Mihajlik
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that…
Scaling Conversational Hungarian ASR: The BEA-Dialogue+ Corpus
Máté Gedeon, Piroska Zsófia Barta, Péter Mihajlik +1
Conversational automatic speech recognition in Hungarian is constrained by the limited amount of publicly available dialogue-style training data. The BEA-Dialogue corpus addresses…
Speaker-Aware Simulation Improves Conversational Speech Recognition
Máté Gedeon, Péter Mihajlik
Automatic speech recognition (ASR) for conversational speech remains challenging due to the limited availability of large-scale, well-annotated multi-speaker dialogue data and the…
Toward Conversational Hungarian Speech Recognition: Introducing the BEA-Large and BEA-Dialogue Datasets
Máté Gedeon, Piroska Zsófia Barta, Péter Mihajlik +3
The advancement of automatic speech recognition (ASR) has been largely enhanced by extensive datasets in high-resource languages, while languages such as Hungarian remain underrepr…
LibriConvo: Simulating Conversations from Read Literature for ASR and Diarization
Máté Gedeon, Péter Mihajlik
We introduce LibriConvo, a synthetic conversational speech corpus for speaker diarization and automatic speech recognition (ASR), built by instantiating the previously proposed Spe…
From Independence to Interaction: Speaker-Aware Simulation of Multi-Speaker Conversational Timing
Máté Gedeon, Péter Mihajlik
We present a speaker-aware approach for simulating multi-speaker conversations that captures temporal consistency and realistic turn-taking dynamics. Prior work typically models ag…