Albeit recent progress in speaker verification generates powerful models,
malicious attacks in the form of spoofed speech, are generally not coped with.
Recent results in ASVSpoof2015 and BTAS2016 challenges indicate that
spoof-aware features are a possible solution to this problem. Most successful
methods in both challenges focus on spoof-aware features, rather than focusing
on a powerful classifier. In this paper we present a novel raw waveform based
deep model for spoofing detection, which jointly acts as a feature extractor
and classifier, thus allowing it to directly classify speech signals. This
approach can be considered as an end-to-end classifier, which removes the need
for any pre- or post-processing on the data, making training and evaluation a
streamlined process, consuming less time than other neural-network based
approaches. The experiments on the BTAS2016 dataset show that the system
performance is significantly improved by the proposed raw waveform
convolutional long short term neural network (CLDNN), from the previous best
published 1.26\% half total error rate (HTER) to the current 0.82\% HTER.
Moreover it shows that the proposed system also performs well under the unknown
(RE-PH2-PH3,RE-LPPH2-PH3) conditions.

本文介绍了一种基于原始波形的深度模型进行欺骗检测的方法，该模型同时作为特征提取器和分类器，消除了数据的任何预处理和后处理，通过使用卷积长短时神经网络（CLDNN），使得在 BTAS2016 数据集上系统性能从最佳 1.26％半总误差率（HTER）显著提高到现在的 0.82％HTER，并表现出在未知条件下也能很好地进行欺骗检测。

基于原始波形 CLDNN 的端到端欺诈检测

End-to-end spoofing detection with raw waveform CLDNNs

We present a causal speech enhancement model working on the raw waveform that
runs in real-time on a laptop CPU. The proposed model is based on an
encoder-decoder architecture with skip-connections. It is optimized on both
time and frequency domains, using multiple loss functions. Empirical evidence
shows that it is capable of removing various kinds of background noise
including stationary and non-stationary noises, as well as room reverb.
Additionally, we suggest a set of data augmentation techniques applied directly
on the raw waveform which further improve model performance and its
generalization abilities. We perform evaluations on several standard
benchmarks, both using objective metrics and human judgements. The proposed
model matches state-of-the-art performance of both causal and non causal
methods while working directly on the raw waveform.

本研究提出了一种基于编码器 - 解码器结构、包含跳跃连接、优化时间和频域的多损失函数的实时语音增强模型，可直接处理原始波形并消除各种背景噪音及实现房间回声，进行了多项标准测试，且通过数据扩增进一步完善模型性能及泛化能力，达到了最先进的性能。

波形域中的实时语音增强

Real Time Speech Enhancement in the Waveform Domain

Conventional automatic speech recognition (ASR) typically performs
multi-level pattern recognition tasks that map the acoustic speech waveform
into a hierarchy of speech units. But, it is widely known that information loss
in the earlier stage can propagate through the later stages. After the
resurgence of deep learning, interest has emerged in the possibility of
developing a purely end-to-end ASR system from the raw waveform to the
transcription without any predefined alignments and hand-engineered models.
However, the successful attempts in end-to-end architecture still used
spectral-based features, while the successful attempts in using raw waveform
were still based on the hybrid deep neural network - Hidden Markov model
(DNN-HMM) framework. In this paper, we construct the first end-to-end
attention-based encoder-decoder model to process directly from raw speech
waveform to the text transcription. We called the model as "Attention-based
Wav2Text". To assist the training process of the end-to-end model, we propose
to utilize a feature transfer learning. Experimental results also reveal that
the proposed Attention-based Wav2Text model directly with raw waveform could
achieve a better result in comparison with the attentional encoder-decoder
model trained on standard front-end filterbank features.

这篇论文介绍了使用基于注意力机制的编码器 - 解码器模型及特征迁移学习来建立一个自动语音识别的端对端模型，通过处理原始语音信号并不需要设计预定义的对齐和手动建立的模型来直接与文本转录进行交互