Using Privacy-Transformed Speech in the Automatic Speech Recognition Acoustic Model Training

• Askars Salimbajevs ^[1]
  1. [1] University of Latvia

     University of Latvia

     Letonia

     
• Localización: Human Language Technologies – The Baltic Perspective: Proceedings of the Ninth International Conference Baltic HLT 2020 / coord. por Andrius Utka, Jurgita Vaičenonienė, Jolanta Kovalevskaitė, Danguolė Kalinauskaitė, 2024, ISBN 978-1-64368-116-0, págs. 47-54
• Idioma: inglés
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• Resumen

  • Automatic Speech Recognition (ASR) requires huge amounts of real user speech data to reach state-of-the-art performance. However, speech data conveys sensitive speaker attributes like identity that can be inferred and exploited for malicious purposes. Therefore, there is an interest in the collection of anonymized speech data that is processed by some voice conversion method. In this paper, we evaluate one of the voice conversion methods on Latvian speech data and also investigate if privacy-transformed data can be used to improve ASR acoustic models. Results show the effectiveness of voice conversion against state-of-theart speaker verification models on Latvian speech and the effectiveness of using privacy-transformed data in ASR training.