Acoustic environments affect acoustic characteristics of sound to be
recognized by physically interacting with sound wave propagation. Thus,
training acoustic models for audio and speech tasks requires regularization on
various acoustic environments in order to achieve robust performance in real
life applications. We propose FilterAugment, a data augmentation method for
regularization of acoustic models on various acoustic environments.
FilterAugment mimics acoustic filters by applying different weights on
frequency bands, therefore enables model to extract relevant information from
wider frequency region. It is an improved version of frequency masking which
masks information on random frequency bands. FilterAugment improved sound event
detection (SED) model performance by 6.50% while frequency masking only
improved 2.13% in terms of polyphonic sound detection score (PSDS). It achieved
equal error rate (EER) of 1.22% when applied to a text-independent speaker
verification model, outperforming model used frequency masking with EER of
1.26%. Prototype of FilterAugment was applied in our participation in DCASE
2021 challenge task 4, and played a major role in achieving the 3rd rank.

提出了一种名为 FilterAugment 的数据增强方法，用于在不同声学环境下正则化音频模型。通过在频率带上应用不同的权重，这种方法可以模拟声学滤波器，从而使模型能够从更广泛的频率区域提取相关信息。实验证明，与频率屏蔽相比，FilterAugment 在声音事件检测性能方面的提升为 6.50％，在说话人验证方面取得了 1.22％的等误率。

FilterAugment: 一种声学环境数据增强方法

FilterAugment: An Acoustic Environmental Data Augmentation Method

In this paper, we present AISHELL-3, a large-scale and high-fidelity
multi-speaker Mandarin speech corpus which could be used to train multi-speaker
Text-to-Speech (TTS) systems. The corpus contains roughly 85 hours of
emotion-neutral recordings spoken by 218 native Chinese mandarin speakers.
Their auxiliary attributes such as gender, age group and native accents are
explicitly marked and provided in the corpus. Accordingly, transcripts in
Chinese character-level and pinyin-level are provided along with the
recordings. We present a baseline system that uses AISHELL-3 for multi-speaker
Madarin speech synthesis. The multi-speaker speech synthesis system is an
extension on Tacotron-2 where a speaker verification model and a corresponding
loss regarding voice similarity are incorporated as the feedback constraint. We
aim to use the presented corpus to build a robust synthesis model that is able
to achieve zero-shot voice cloning. The system trained on this dataset also
generalizes well on speakers that are never seen in the training process.
Objective evaluation results from our experiments show that the proposed
multi-speaker synthesis system achieves high voice similarity concerning both
speaker embedding similarity and equal error rate measurement. The dataset,
baseline system code and generated samples are available online.

本论文介绍了 AISHELL-3 数据集，用于训练多扬声器普通话语音合成系统，同时介绍了一种基于 Tacotron-2 的多扬声器语音合成基线模型，该模型结合了说话人验证模型和相应的语音相似性损失作为反馈约束。该论文旨在利用所提供的语音数据集构建鲁棒的语音合成模型，并实现零 - shot 语音克隆。实验结果表明，该多说话人合成系统在声纹嵌入相似性和等误差率测量方面均能实现较高的语音相似度。