Children speech recognition is indispensable but challenging due to the
diversity of children's speech. In this paper, we propose a filter-based
discriminative autoencoder for acoustic modeling. To filter out the influence
of various speaker types and pitches, auxiliary information of the speaker and
pitch features is input into the encoder together with the acoustic features to
generate phonetic embeddings. In the training phase, the decoder uses the
auxiliary information and the phonetic embedding extracted by the encoder to
reconstruct the input acoustic features. The autoencoder is trained by
simultaneously minimizing the ASR loss and feature reconstruction error. The
framework can make the phonetic embedding purer, resulting in more accurate
senone (triphone-state) scores. Evaluated on the test set of the CMU Kids
corpus, our system achieves a 7.8% relative WER reduction compared to the
baseline system. In the domain adaptation experiment, our system also
outperforms the baseline system on the British-accent PF-STAR task.

本文提出了一种基于滤波的判别性自编码器技术，旨在解决儿童语音识别中多种说话人类型和音高的影响问题，并在测试集上实现了较基准系统更高的识别率，并在英国口音 PF-STAR 任务中实现了比基线系统更好的结果。

面向儿童语音识别的基于滤波器的判别自编码器

Filter-based Discriminative Autoencoders for Children Speech Recognition

A key desiderata for inclusive and accessible speech recognition technology
is ensuring its robust performance to children's speech. Notably, this includes
the rapidly advancing neural network based end-to-end speech recognition
systems. Children speech recognition is more challenging due to the larger
intra-inter speaker variability in terms of acoustic and linguistic
characteristics compared to adult speech. Furthermore, the lack of adequate and
appropriate children speech resources adds to the challenge of designing robust
end-to-end neural architectures. This study provides a critical assessment of
automatic children speech recognition through an empirical study of
contemporary state-of-the-art end-to-end speech recognition systems. Insights
are provided on the aspects of training data requirements, adaptation on
children data, and the effect of children age, utterance lengths, different
architectures and loss functions for end-to-end systems and role of language
models on the speech recognition performance.

通过对最先进的端到端语音识别系统的实证研究，本研究对自动儿童语音识别进行了关键性的评估，并提供了关于训练数据需求、适应儿童数据、儿童年龄、语音长度、不同架构和损失函数对端到端系统的影响以及语言模型对语音识别性能的作用的见解。