RawSpectrogram: On the Way to Effective Streaming Speech Anti-Spoofing

Traditional anti-spoofing systems cannot be used straightforwardly with streaming audio because they are designed for finite utterances. Such offline models can be applied in streaming with the help of buffering; however, they are not effective in terms of memory and computational consumption. We pr...
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Gespeichert in:

Autor*in:	Petr Grinberg [verfasserIn] Vladislav Shikhov [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Anti-spoofing ASVspoof challenge automatic speaker verification system countermeasure SASV challenge spoofing-aware speaker verification system

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 109928-109938
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:109928-109938

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3321919

Katalog-ID:	DOAJ096760230

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callnumber-first	T - Technology
author	Petr Grinberg
spellingShingle	Petr Grinberg misc TK1-9971 misc Anti-spoofing misc ASVspoof challenge misc automatic speaker verification system misc countermeasure misc SASV challenge misc spoofing-aware speaker verification system misc Electrical engineering. Electronics. Nuclear engineering RawSpectrogram: On the Way to Effective Streaming Speech Anti-Spoofing
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topic_title	TK1-9971 RawSpectrogram: On the Way to Effective Streaming Speech Anti-Spoofing Anti-spoofing ASVspoof challenge automatic speaker verification system countermeasure SASV challenge spoofing-aware speaker verification system
topic	misc TK1-9971 misc Anti-spoofing misc ASVspoof challenge misc automatic speaker verification system misc countermeasure misc SASV challenge misc spoofing-aware speaker verification system misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Anti-spoofing misc ASVspoof challenge misc automatic speaker verification system misc countermeasure misc SASV challenge misc spoofing-aware speaker verification system misc Electrical engineering. Electronics. Nuclear engineering
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title	RawSpectrogram: On the Way to Effective Streaming Speech Anti-Spoofing
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title_full	RawSpectrogram: On the Way to Effective Streaming Speech Anti-Spoofing
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title_sort	rawspectrogram: on the way to effective streaming speech anti-spoofing
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title_auth	RawSpectrogram: On the Way to Effective Streaming Speech Anti-Spoofing
abstract	Traditional anti-spoofing systems cannot be used straightforwardly with streaming audio because they are designed for finite utterances. Such offline models can be applied in streaming with the help of buffering; however, they are not effective in terms of memory and computational consumption. We propose a novel approach called RawSpectrogram that makes offline models streaming-friendly without a significant drop in quality. The method was tested on RawNet2 and AASIST, resulting in new architectures called RawRNN (RawLSTM and RawGRU), RS-AASIST, and TAASIST. The RawRNN-type models are much smaller and achieve a better Equal Error Rate than their base architecture, RawNet2. RS-AASIST and TAASIST have fewer parameters than AASIST and achieve similar quality. We also proved our concept for models with time-frequency transform front-ends and automatic speaker verification systems by proposing RECAPA-TDNN based on ECAPA-TDNN. RS-AASIST and RECAPA-TDNN were combined into the first streaming-friendly spoofing-aware speaker verification system reported in the literature. This joint system achieves significantly better quality than the corresponding offline solutions. All our models require far fewer floating-point operations for score updates. RawSpectrogram usage significantly reduces the latency of the prediction and allows the system to update the probability with each new chunk from the stream, preserving all information from the past. To the best of our knowledge, TAASIST is the most successful voice anti-spoofing system that employs a vanilla Transformer trained using supervised learning.
abstractGer	Traditional anti-spoofing systems cannot be used straightforwardly with streaming audio because they are designed for finite utterances. Such offline models can be applied in streaming with the help of buffering; however, they are not effective in terms of memory and computational consumption. We propose a novel approach called RawSpectrogram that makes offline models streaming-friendly without a significant drop in quality. The method was tested on RawNet2 and AASIST, resulting in new architectures called RawRNN (RawLSTM and RawGRU), RS-AASIST, and TAASIST. The RawRNN-type models are much smaller and achieve a better Equal Error Rate than their base architecture, RawNet2. RS-AASIST and TAASIST have fewer parameters than AASIST and achieve similar quality. We also proved our concept for models with time-frequency transform front-ends and automatic speaker verification systems by proposing RECAPA-TDNN based on ECAPA-TDNN. RS-AASIST and RECAPA-TDNN were combined into the first streaming-friendly spoofing-aware speaker verification system reported in the literature. This joint system achieves significantly better quality than the corresponding offline solutions. All our models require far fewer floating-point operations for score updates. RawSpectrogram usage significantly reduces the latency of the prediction and allows the system to update the probability with each new chunk from the stream, preserving all information from the past. To the best of our knowledge, TAASIST is the most successful voice anti-spoofing system that employs a vanilla Transformer trained using supervised learning.
abstract_unstemmed	Traditional anti-spoofing systems cannot be used straightforwardly with streaming audio because they are designed for finite utterances. Such offline models can be applied in streaming with the help of buffering; however, they are not effective in terms of memory and computational consumption. We propose a novel approach called RawSpectrogram that makes offline models streaming-friendly without a significant drop in quality. The method was tested on RawNet2 and AASIST, resulting in new architectures called RawRNN (RawLSTM and RawGRU), RS-AASIST, and TAASIST. The RawRNN-type models are much smaller and achieve a better Equal Error Rate than their base architecture, RawNet2. RS-AASIST and TAASIST have fewer parameters than AASIST and achieve similar quality. We also proved our concept for models with time-frequency transform front-ends and automatic speaker verification systems by proposing RECAPA-TDNN based on ECAPA-TDNN. RS-AASIST and RECAPA-TDNN were combined into the first streaming-friendly spoofing-aware speaker verification system reported in the literature. This joint system achieves significantly better quality than the corresponding offline solutions. All our models require far fewer floating-point operations for score updates. RawSpectrogram usage significantly reduces the latency of the prediction and allows the system to update the probability with each new chunk from the stream, preserving all information from the past. To the best of our knowledge, TAASIST is the most successful voice anti-spoofing system that employs a vanilla Transformer trained using supervised learning.
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title_short	RawSpectrogram: On the Way to Effective Streaming Speech Anti-Spoofing
url	https://doi.org/10.1109/ACCESS.2023.3321919 https://doaj.org/article/986cc4060ce34aef922488f7ed5264b3 https://ieeexplore.ieee.org/document/10271307/ https://doaj.org/toc/2169-3536
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author2	Vladislav Shikhov
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