Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space
The use of synthetic speech as data augmentation is gaining increasing popularity in fields such as automatic speech recognition and speech classification tasks. Despite novel text-to-speech systems with voice cloning capabilities, that allow the usage of a larger amount of voices based on short aud...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
19.09.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The use of synthetic speech as data augmentation is gaining increasing
popularity in fields such as automatic speech recognition and speech
classification tasks. Despite novel text-to-speech systems with voice cloning
capabilities, that allow the usage of a larger amount of voices based on short
audio segments, it is known that these systems tend to hallucinate and
oftentimes produce bad data that will most likely have a negative impact on the
downstream task. In the present work, we conduct a set of experiments around
zero-shot learning with synthetic speech data for the specific task of speech
commands classification. Our results on the Google Speech Commands dataset show
that a simple ASR-based filtering method can have a big impact in the quality
of the generated data, translating to a better performance. Furthermore,
despite the good quality of the generated speech data, we also show that
synthetic and real speech can still be easily distinguishable when using
self-supervised (WavLM) features, an aspect further explored with a CycleGAN to
bridge the gap between the two types of speech material. |
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DOI: | 10.48550/arxiv.2409.12745 |