6533b827fe1ef96bd1287262
RESEARCH PRODUCT
Using privacy-transformed speech in the automatic speech recognition acoustic model training
Askars Salimbajevssubject
Speaker verificationevaluationvoice conversionComputer scienceSpeech recognitionautomatic speech recognitionLatvianAcoustic model[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]privacylanguage.human_language[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]anonymization[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL]Identity (object-oriented programming)languageConversion methodautomatic speaker verificationdescription
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-the-art speaker verification models on Latvian speech and the effectiveness of using privacy-transformed data in ASR training.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 |