Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition
This addresses data scarcity and privacy issues in sensitive conversational domains, though it is incremental as it builds on existing synthetic data methods.
The paper tackles the problem of generating synthetic data for multi-speaker conversational speech recognition by proposing a pipeline that uses a large language model for content creation and a conversational multi-speaker text-to-speech model for synthesis, resulting in significant performance improvements over classical approaches.
Currently, a common approach in many speech processing tasks is to leverage large scale pre-trained models by fine-tuning them on in-domain data for a particular application. Yet obtaining even a small amount of such data can be problematic, especially for sensitive domains and conversational speech scenarios, due to both privacy issues and annotation costs. To address this, synthetic data generation using single speaker datasets has been employed. Yet, for multi-speaker cases, such an approach often requires extensive manual effort and is prone to domain mismatches. In this work, we propose a synthetic data generation pipeline for multi-speaker conversational ASR, leveraging a large language model (LLM) for content creation and a conversational multi-speaker text-to-speech (TTS) model for speech synthesis. We conduct evaluation by fine-tuning the Whisper ASR model for telephone and distant conversational speech settings, using both in-domain data and generated synthetic data. Our results show that the proposed method is able to significantly outperform classical multi-speaker generation approaches that use external, non-conversational speech datasets.