On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures
This work addresses the problem of data scarcity for ASR training by exploring synthetic data, but it is incremental as it builds on prior research with ablation studies.
The study investigated the effectiveness of using purely synthetic speech data for training automatic speech recognition (ASR) systems, finding that different ASR architectures show varying sensitivity to synthetic data, with TTS models generalizing well despite overfitting indicators.
In this work we evaluate the utility of synthetic data for training automatic speech recognition (ASR). We use the ASR training data to train a text-to-speech (TTS) system similar to FastSpeech-2. With this TTS we reproduce the original training data, training ASR systems solely on synthetic data. For ASR, we use three different architectures, attention-based encoder-decoder, hybrid deep neural network hidden Markov model and a Gaussian mixture hidden Markov model, showing the different sensitivity of the models to synthetic data generation. In order to extend previous work, we present a number of ablation studies on the effectiveness of synthetic vs. real training data for ASR. In particular we focus on how the gap between training on synthetic and real data changes by varying the speaker embedding or by scaling the model size. For the latter we show that the TTS models generalize well, even when training scores indicate overfitting.