With the huge technological advances introduced by deep learning in audio &
speech processing, many novel synthetic speech techniques achieved incredible
realistic results. As these methods generate realistic fake human voices, they
can be used in malicious acts such as people imitation, fake news, spreading,
spoofing, media manipulations, etc. Hence, the ability to detect synthetic or
natural speech has become an urgent necessity. Moreover, being able to tell
which algorithm has been used to generate a synthetic speech track can be of
preeminent importance to track down the culprit. In this paper, a novel
strategy is proposed to attribute a synthetic speech track to the generator
that is used to synthesize it. The proposed detector transforms the audio into
log-mel spectrogram, extracts features using CNN, and classifies it between
five known and unknown algorithms, utilizing semi-supervision and ensemble to
improve its robustness and generalizability significantly. The proposed
detector is validated on two evaluation datasets consisting of a total of
18,000 weakly perturbed (Eval 1) & 10,000 strongly perturbed (Eval 2) synthetic
speeches. The proposed method outperforms other top teams in accuracy by 12-13%
on Eval 2 and 1-2% on Eval 1, in the IEEE SP Cup challenge at ICASSP 2022.

通过将语音转化为对数梅尔频谱图，利用半监督学习和集成方法来提高稳健性和泛化性，本文提出了一种将合成语音与生成器相关联的新策略，并在 IEEE SP Cup 2022 的 ICASSP 挑战中，准确率相较 Eval 2 提高了 12-13%，Eval 1 提高了 1-2%。

Syn-Att：通过半监督的未知多类 CNN 集成进行合成音频归属

Syn-Att: Synthetic Speech Attribution via Semi-Supervised Unknown  Multi-Class Ensemble of CNNs

Self-supervised learning, especially contrastive learning, has made an
outstanding contribution to the development of many deep learning research
fields. Recently, researchers in the acoustic signal processing field noticed
its success and leveraged contrastive learning for better music representation.
Typically, existing approaches maximize the similarity between two distorted
audio segments sampled from the same music. In other words, they ensure a
semantic agreement at the music level. However, those coarse-grained methods
neglect some inessential or noisy elements at the frame level, which may be
detrimental to the model to learn the effective representation of music.
Towards this end, this paper proposes a novel Positive-nEgative frame mask for
Music Representation based on the contrastive learning framework, abbreviated
as PEMR. Concretely, PEMR incorporates a Positive-Negative Mask Generation
module, which leverages transformer blocks to generate frame masks on the
Log-Mel spectrogram. We can generate self-augmented negative and positive
samples by masking important components or inessential components,
respectively. We devise a novel contrastive learning objective to accommodate
both self-augmented positives/negatives sampled from the same music. We conduct
experiments on four public datasets. The experimental results of two
music-related downstream tasks, music classification, and cover song
identification, demonstrate the generalization ability and transferability of
music representation learned by PEMR.

本文提出了基于对比学习的音乐表示的新方法 PEMR，它具有正负帧层级掩码生成模块和用于获得关于正负音频片段相似性的对比学习目标，该方法与现有方法相比更有效地学习音乐的有效表示。

使用正负框架掩码的对比学习用于音乐表示

Contrastive Learning with Positive-Negative Frame Mask for Music  Representation

Language Identification (LI) is an important first step in several speech
processing systems. With a growing number of voice-based assistants, speech LI
has emerged as a widely researched field. To approach the problem of
identifying languages, we can either adopt an implicit approach where only the
speech for a language is present or an explicit one where text is available
with its corresponding transcript. This paper focuses on an implicit approach
due to the absence of transcriptive data. This paper benchmarks existing models
and proposes a new attention based model for language identification which uses
log-Mel spectrogram images as input. We also present the effectiveness of raw
waveforms as features to neural network models for LI tasks. For training and
evaluation of models, we classified six languages (English, French, German,
Spanish, Russian and Italian) with an accuracy of 95.4% and four languages
(English, French, German, Spanish) with an accuracy of 96.3% obtained from the
VoxForge dataset. This approach can further be scaled to incorporate more
languages.

该研究论文研究了语言识别的问题，使用了基于注意力机制和神经网络的方法，使用频谱图像作为输入以及原始波形作为特征，对六种语言进行了分类，获得了高精度的结果。