Efficient ASR Training with Conversations that Never Happened

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Audio and Speech Processing · Depth: Expert, quick

Summary

An augmentation pipeline has been developed to address the scarcity of domain-matched multi-speaker training data for conversational Automatic Speech Recognition (ASR) in lower-resource languages and niche domains. This pipeline generates scenario-level dialogues, maps speaker attributes to Text-to-Speech (TTS) voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations. Researchers evaluated five LLM families using a FastConformer-Large training recipe on the Hungarian BEA-Dialogue benchmark corpus. The method is applicable to any language given the necessary component resources. Results consistently show that synthetic conversations improve speech recognition performance, though generator choice and data composition significantly influence gains. Notably, a configuration using only 67 hours of real conversations and 636 hours of simulated data achieved superior performance compared to a zero-shot model trained on 2700 hours of Hungarian speech. This indicates LLM-generated conversational data, synthesized with TTS, effectively complements real conversational corpora for speech model training.

Key takeaway

For Machine Learning Engineers developing ASR models in lower-resource languages or niche domains, you should integrate LLM-generated synthetic conversational data to overcome data scarcity. This approach, demonstrated by outperforming models trained on 2700 hours of real speech with only 67 hours of real and 636 hours of simulated data, offers a practical path to enhance performance. Prioritize careful selection of your LLM generator and optimize data composition for maximum gains.

Key insights

LLM-generated synthetic conversations, synthesized via TTS, effectively augment scarce real data for ASR training.

Principles

• Synthetic conversational data consistently improves ASR performance.
• Generator choice and data composition critically affect performance gains.

Method

An augmentation pipeline generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.

In practice

• Combine 67 hours of real data with 636 hours of simulated data for ASR.
• Evaluate multiple LLM families for synthetic dialogue generation.

Topics

• ASR Training
• Data Augmentation
• Large Language Models
• Text-to-Speech
• Conversational AI
• Low-Resource Languages

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.