Efficient ASR Training with Conversations that Never Happened
Abstract
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 generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations. We evaluated five LLM families under single-generator, fixed-budget mixture, and scale-up settings using the same FastConformer-Large training recipe for each one. We ran comprehensive evaluations on the Hungarian BEA-Dialogue benchmark corpus, with the method itself being applicable to any language given the resources for each component. The results show that synthetic conversations consistently improve speech recognition performance, but generator choice and data composition strongly affect the gains. Our largest training configuration, using only 67 hours of real conversations and 636 hours of simulated data, achieves better performance on the evaluation benchmark than a zero-shot model trained on 2700 hours of Hungarian speech. These findings indicate that LLM-generated conversational data synthesized with TTS is a practical complement to real conversational corpora for speech model training.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel augmentation pipeline that leverages LLM-generated conversational data to enhance ASR training for low-resource languages. This comprehensive approach addresses critical data scarcity issues and demonstrates significant performance improvements, marking a meaningful contribution to the field of machine learning and speech recognition.
Methodology
The proposed methodology introduces a novel three-stage pipeline for generating synthetic conversational audio data for ASR training. This pipeline effectively integrates LLM-generated scenario and dialogue creation with metadata-conditioned TTS synthesis and speaker-aware conversation simulation. The approach addresses the limitations of existing augmentation techniques by providing a structured method to generate diverse and contextually rich conversational data, which is particularly beneficial for low-resource languages. The methodology is well-articulated, detailing each stage's processes and the rationale behind them, although the lack of detailed equations and citations in some areas could enhance clarity.
Results & evaluation
The experiments are comprehensive, utilizing multiple LLM families and varying configurations to assess the impact of synthetic data on ASR performance. The use of the Hungarian BEA-Dialogue benchmark corpus is appropriate, and the evaluation metrics (cpCER and cpWER) are relevant for measuring ASR accuracy. The results demonstrate significant improvements over baseline models, indicating the effectiveness of the proposed augmentation method. However, the paper could benefit from more detailed statistical analysis and comparisons with additional baselines to strengthen the claims.
Reproducibility
The paper provides a clear description of the experimental setup, including the ASR architecture and training recipe, which aids reproducibility. However, the absence of publicly available code or datasets limits the ability to fully replicate the experiments. Providing access to the synthetic data generation pipeline and the trained models would enhance reproducibility.
Limitations
One limitation is the reliance on specific LLMs and TTS systems, which may not be universally applicable across all languages and domains. Additionally, while the results show improvements, the paper does not address potential biases in the generated data or the quality of the synthetic conversations compared to real interactions. The performance gains observed may not generalize to other languages or domains without further validation.
Broader impact
The findings have significant implications for the field of ASR, particularly in enhancing the training of models for low-resource languages and niche domains. By demonstrating that synthetic conversational data can effectively complement real data, this work opens avenues for more efficient ASR training methodologies. The approach could also be adapted for other applications, such as dialogue systems and conversational agents, potentially improving their robustness and performance in diverse settings. The paper presents a novel augmentation pipeline that leverages LLM-generated conversational data to enhance ASR training for low-resource languages. This comprehensive approach addresses critical data scarcity issues and demonstrates significant performance improvements, marking a meaningful contribution to the field of machine learning and speech recognition.
Author of this paper? Send a correction.