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Study reveals conversational timing impacts synthetic ASR training data

By PulseAugur Editorial · [2 sources] ·

This paper investigates the impact of conversational timing on synthetic data used for training Automatic Speech Recognition (ASR) systems. Researchers explored a four-dimensional parameter space related to pause and overlap timing, generating simulated conversations to train ASR models. The study found that higher overlap in simulated data correlates with lower word error rates, while longer and more variable pauses increase errors. Bayesian optimization provided analytical insights into this overlap-gap trade-off, suggesting that task-relevant diagnostics of timing profiles are crucial for improving simulated training data. AI

IMPACT This research offers insights into optimizing synthetic data generation for ASR, potentially leading to more efficient and accurate speech recognition systems.

RANK_REASON The cluster contains an academic paper published on arXiv.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Study reveals conversational timing impacts synthetic ASR training data

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · M\'at\'e Gedeon, P\'eter Mihajlik ·

    On the Role of Conversational Timing in Synthetic Training Data for ASR

    arXiv:2607.08371v1 Announce Type: cross Abstract: Synthetic multi-speaker conversations are widely used to train conversational automatic speech recognition (ASR) systems, but it remains unclear which timing properties make simulated data most useful. This paper studies conversat…

  2. arXiv cs.AI TIER_1 English(EN) · Péter Mihajlik ·

    On the Role of Conversational Timing in Synthetic Training Data for ASR

    Synthetic multi-speaker conversations are widely used to train conversational automatic speech recognition (ASR) systems, but it remains unclear which timing properties make simulated data most useful. This paper studies conversational timing as a controllable training variable r…

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