Synthetic voice and splicing audio clips have been generated to spoof
Internet users and artificial intelligence (AI) technologies such as voice
authentication. Existing research work treats spoofing countermeasures as a
binary classification problem: bonafide vs. spoof. This paper extends the
existing Res2Net by involving the recent Conformer block to further exploit the
local patterns on acoustic features. Experimental results on ASVspoof 2019
database show that the proposed SE-Res2Net-Conformer architecture is able to
improve the spoofing countermeasures performance for the logical access
scenario.
In addition, this paper also proposes to re-formulate the existing audio
splicing detection problem. Instead of identifying the complete splicing
segments, it is more useful to detect the boundaries of the spliced segments.
Moreover, a deep learning approach can be used to solve the problem, which is
different from the previous signal processing techniques.

本文提出了一种使用 Res2Net 和 Conformer block 相结合的架构来改进声纹鉴定的欺骗对策，同时也提出了一种新的音频拼接检测问题的深度学习方法，该方法可以检测拼接边界而不是完整的拼接片段。

利用 SE-Res2Net-Conformer 结构进行合成语音检测和音频剪辑检测

Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecture

This paper describes the Microsoft end-to-end neural text to speech (TTS)
system: DelightfulTTS for Blizzard Challenge 2021. The goal of this challenge
is to synthesize natural and high-quality speech from text, and we approach
this goal in two perspectives: The first is to directly model and generate
waveform in 48 kHz sampling rate, which brings higher perception quality than
previous systems with 16 kHz or 24 kHz sampling rate; The second is to model
the variation information in speech through a systematic design, which improves
the prosody and naturalness. Specifically, for 48 kHz modeling, we predict 16
kHz mel-spectrogram in acoustic model, and propose a vocoder called HiFiNet to
directly generate 48 kHz waveform from predicted 16 kHz mel-spectrogram, which
can better trade off training efficiency, modelling stability and voice
quality. We model variation information systematically from both explicit
(speaker ID, language ID, pitch and duration) and implicit (utterance-level and
phoneme-level prosody) perspectives: 1) For speaker and language ID, we use
lookup embedding in training and inference; 2) For pitch and duration, we
extract the values from paired text-speech data in training and use two
predictors to predict the values in inference; 3) For utterance-level and
phoneme-level prosody, we use two reference encoders to extract the values in
training, and use two separate predictors to predict the values in inference.
Additionally, we introduce an improved Conformer block to better model the
local and global dependency in acoustic model. For task SH1, DelightfulTTS
achieves 4.17 mean score in MOS test and 4.35 in SMOS test, which indicates the
effectiveness of our proposed system

本文描述了 Microsoft 的端到端神经文本语音合成系统：用于 Blizzard Challenge 2021 的 DelightfulTTS。该系统在两个方面实现了将文本合成成自然且高质量的语音：一方面是直接建模和生成 48 kHz 采样率的波形，另一方面是通过系统化设计来建模语音中的变化信息，从而提高韵律和自然度。