A synthetic-data pipeline for medical ASR reports sub-1% WER on standard benchmarks, but the reported numbers are internally inconsistent and not reproducible from the paper.
Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Dialogue state tracking plays a crucial role in extracting information in task-oriented dialogue systems. However, preceding research are limited to textual modalities, primarily due to the shortage of authentic human audio datasets. We address this by investigating synthetic audio data for audio-based DST. To this end, we develop cascading and end-to-end models, train them with our synthetic audio dataset, and test them on actual human speech data. To facilitate evaluation tailored to audio modalities, we introduce a novel PhonemeF1 to capture pronunciation similarity. Experimental results showed that models trained solely on synthetic datasets can generalize their performance to human voice data. By eliminating the dependency on human speech data collection, these insights pave the way for significant practical advancements in audio-based DST. Data and code are available at https://github.com/JihyunLee1/E2E-DST.
fields
eess.AS 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR
A synthetic-data pipeline for medical ASR reports sub-1% WER on standard benchmarks, but the reported numbers are internally inconsistent and not reproducible from the paper.