Identifying user-defined keywords is crucial for personalizing interactions
with smart devices. Previous approaches of user-defined keyword spotting
(UDKWS) have relied on short-term spectral features such as mel frequency
cepstral coefficients (MFCC) to detect the spoken keyword. However, these
features may face challenges in accurately identifying closely related
pronunciation of audio-text pairs, due to their limited capability in capturing
the temporal dynamics of the speech signal. To address this challenge, we
propose to use shifted delta coefficients (SDC) which help in capturing
pronunciation variability (transition between connecting phonemes) by
incorporating long-term temporal information. The performance of the SDC
feature is compared with various baseline features across four different
datasets using a cross-attention based end-to-end system. Additionally, various
configurations of SDC are explored to find the suitable temporal context for
the UDKWS task. The experimental results reveal that the SDC feature
outperforms the MFCC baseline feature, exhibiting an improvement of 8.32% in
area under the curve (AUC) and 8.69% in terms of equal error rate (EER) on the
challenging Libriphrase-hard dataset. Moreover, the proposed approach
demonstrated superior performance when compared to state-of-the-art UDKWS
techniques.

通过使用移位增量系数（SDC）捕捉发音变异性并结合长时信息，该研究表明，SDC 特征相对于短时谱特征（如 MFCC）在用户定义关键词识别任务中取得了更好的表现，其 AUC 指标提高了 8.32%，等错误率（EER）降低了 8.69%。

使用偏移增量系数的端到端用户定义关键词检测

End-to-End User-Defined Keyword Spotting using Shifted Delta  Coefficients

In this paper, we propose a novel end-to-end user-defined keyword spotting
method that utilizes linguistically corresponding patterns between speech and
text sequences. Unlike previous approaches requiring speech keyword enrollment,
our method compares input queries with an enrolled text keyword sequence. To
place the audio and text representations within a common latent space, we adopt
an attention-based cross-modal matching approach that is trained in an
end-to-end manner with monotonic matching loss and keyword classification loss.
We also utilize a de-noising loss for the acoustic embedding network to improve
robustness in noisy environments. Additionally, we introduce the LibriPhrase
dataset, a new short-phrase dataset based on LibriSpeech for efficiently
training keyword spotting models. Our proposed method achieves competitive
results on various evaluation sets compared to other single-modal and
cross-modal baselines.

本文提出一种新颖的端到端的用户定义关键词检测方法，利用语音和文本序列之间的语言对应模式，实现音频和文本表征在共同的隐含空间中；采用基于注意力的跨模态匹配方法，训练具有单调匹配损失和关键词分类损失的模型，并利用去噪损失改善模型在嘈杂环境中的稳健性，同时引入 LibriPhrase 数据集用于高效地训练关键词检测模型；与其他单模态和跨模态基线相比，在各种评估集上取得了有竞争力的结果。