Sum-product networks for robust automatic speaker identification
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Author(s)
Nicolson, A
Paliwal, KK
Griffith University Author(s)
Year published
2020
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We introduce sum-product networks (SPNs) for robust speech processing through a simple robust automatic speaker identification (ASI) task.* SPNs are deep probabilistic graphical models capable of answering multiple probabilistic queries. We show that SPNs are able to remain robust by using the marginal probability density function (PDF) of the spectral features that reliably represent speech. Though current SPN toolkits and learning algorithms are in their infancy, we aim to show that SPNs have the potential to become a useful tool for robust speech processing in the future. SPN speaker models are evaluated here on real-world ...
View more >We introduce sum-product networks (SPNs) for robust speech processing through a simple robust automatic speaker identification (ASI) task.* SPNs are deep probabilistic graphical models capable of answering multiple probabilistic queries. We show that SPNs are able to remain robust by using the marginal probability density function (PDF) of the spectral features that reliably represent speech. Though current SPN toolkits and learning algorithms are in their infancy, we aim to show that SPNs have the potential to become a useful tool for robust speech processing in the future. SPN speaker models are evaluated here on real-world non-stationary and coloured noise sources at multiple signal-to-noise ratio (SNR) levels. In terms of ASI accuracy, we find that SPN speaker models are more robust than two recent convolutional neural network (CNN)-based ASI systems. Additionally, SPN speaker models consist of significantly fewer parameters than their CNN-based counterparts. The results indicate that SPN speaker models could be a robust, parameter-efficient alternative for ASI. Additionally, this work demonstrates that SPNs have potential in related tasks, such as robust automatic speech recognition (ASR) and automatic speaker verification (ASV).
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View more >We introduce sum-product networks (SPNs) for robust speech processing through a simple robust automatic speaker identification (ASI) task.* SPNs are deep probabilistic graphical models capable of answering multiple probabilistic queries. We show that SPNs are able to remain robust by using the marginal probability density function (PDF) of the spectral features that reliably represent speech. Though current SPN toolkits and learning algorithms are in their infancy, we aim to show that SPNs have the potential to become a useful tool for robust speech processing in the future. SPN speaker models are evaluated here on real-world non-stationary and coloured noise sources at multiple signal-to-noise ratio (SNR) levels. In terms of ASI accuracy, we find that SPN speaker models are more robust than two recent convolutional neural network (CNN)-based ASI systems. Additionally, SPN speaker models consist of significantly fewer parameters than their CNN-based counterparts. The results indicate that SPN speaker models could be a robust, parameter-efficient alternative for ASI. Additionally, this work demonstrates that SPNs have potential in related tasks, such as robust automatic speech recognition (ASR) and automatic speaker verification (ASV).
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Conference Title
Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech)
Volume
2020-October
Funder(s)
ARC
Grant identifier(s)
DP170102907
Copyright Statement
© 2020 ISCA and the Author(s). The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference’s website or contact the author(s).
Subject
Artificial intelligence
eess.AS
cs.SD