We present AmberNet, a compact end-to-end neural network for Spoken Language
Identification. AmberNet consists of 1D depth-wise separable convolutions and
Squeeze-and-Excitation layers with global context, followed by statistics
pooling and linear layers. AmberNet achieves performance similar to
state-of-the-art(SOTA) models on VoxLingua107 dataset, while being 10x smaller.
AmberNet can be adapted to unseen languages and new acoustic conditions with
simple finetuning. It attains SOTA accuracy of 75.8% on FLEURS benchmark. We
show the model is easily scalable to achieve a better trade-off between
accuracy and speed. We further inspect the model's sensitivity to input length
and show that AmberNet performs well even on short utterances.

AmberNet 是一种用于口语识别的紧凑端到端神经网络，由一维深度可分离卷积和全局上下文的 Squeeze-and-Excitation 层、统计汇聚和线性层组成，大小是现有技术 (SOTA) 模型的 1/10，准确率达到 FLEURS 基准的 SOTA，可用于新语言和音频条件的简单微调。它在输入长度敏感性方面的表现良好。

AmberNet: 一种紧凑的端到端口语识别模型

AmberNet: A Compact End-to-End Model for Spoken Language Identification

Current speaker verification techniques rely on a neural network to extract
speaker representations. The successful x-vector architecture is a Time Delay
Neural Network (TDNN) that applies statistics pooling to project
variable-length utterances into fixed-length speaker characterizing embeddings.
In this paper, we propose multiple enhancements to this architecture based on
recent trends in the related fields of face verification and computer vision.
Firstly, the initial frame layers can be restructured into 1-dimensional
Res2Net modules with impactful skip connections. Similarly to SE-ResNet, we
introduce Squeeze-and-Excitation blocks in these modules to explicitly model
channel interdependencies. The SE block expands the temporal context of the
frame layer by rescaling the channels according to global properties of the
recording. Secondly, neural networks are known to learn hierarchical features,
with each layer operating on a different level of complexity. To leverage this
complementary information, we aggregate and propagate features of different
hierarchical levels. Finally, we improve the statistics pooling module with
channel-dependent frame attention. This enables the network to focus on
different subsets of frames during each of the channel's statistics estimation.
The proposed ECAPA-TDNN architecture significantly outperforms state-of-the-art
TDNN based systems on the VoxCeleb test sets and the 2019 VoxCeleb Speaker
Recognition Challenge.

本文提出了一种基于统计汇聚、1D Res2Net 模块和 Squeeze-and-Excitation 机制等增强方法的 ECAPA-TDNN 神经网络架构，利用此架构改进了当前流行的 X-Vector 神经网络架构，显著提高了说话人识别性能。

基于 TDNN 的说话人验证中的强调通道关注和传递聚合 (ECAPA-TDNN)

ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in  TDNN Based Speaker Verification

This paper proposes attentive statistics pooling for deep speaker embedding
in text-independent speaker verification. In conventional speaker embedding,
frame-level features are averaged over all the frames of a single utterance to
form an utterance-level feature. Our method utilizes an attention mechanism to
give different weights to different frames and generates not only weighted
means but also weighted standard deviations. In this way, it can capture
long-term variations in speaker characteristics more effectively. An evaluation
on the NIST SRE 2012 and the VoxCeleb data sets shows that it reduces equal
error rates (EERs) from the conventional method by 7.5% and 8.1%, respectively.

本文提出了一种基于关注机制统计池化的深度说话人嵌入，在 NIST SRE 2012 和 VoxCeleb 数据集上的评估结果显示，它在说话人验证任务中比传统方法减少了 7.5% 和 8.1% 的等错误率，其适配长期说话特征变化的能力更强。