Children's speech recognition is considered a low-resource task mainly due to
the lack of publicly available data. There are several reasons for such data
scarcity, including expensive data collection and annotation processes, and
data privacy, among others. Transforming speech signals into discrete tokens
that do not carry sensitive information but capture both linguistic and
acoustic information could be a solution for privacy concerns. In this study,
we investigate the integration of discrete speech tokens into children's speech
recognition systems as input without significantly degrading the ASR
performance. Additionally, we explored single-view and multi-view strategies
for creating these discrete labels. Furthermore, we tested the models for
generalization capabilities with unseen domain and nativity dataset. Results
reveal that the discrete token ASR for children achieves nearly equivalent
performance with an approximate 83% reduction in parameters.

在这项研究中，我们调查了将离散语音标记整合到儿童语音识别系统中的方法，以解决数据稀缺和隐私问题，并探索了单视图和多视图策略，测试了模型的泛化能力。结果表明，儿童的离散标记语音识别系统几乎可以达到与传统方法相当的性能，并且参数减少了约 83%。

儿童语音识别的离散标记增强

Children's Speech Recognition through Discrete Token Enhancement

Discrete speech tokens have been more and more popular in multiple speech
processing fields, including automatic speech recognition (ASR), text-to-speech
(TTS) and singing voice synthesis (SVS). In this paper, we describe the systems
developed by the SJTU X-LANCE group for the TTS (acoustic + vocoder), SVS, and
ASR tracks in the Interspeech 2024 Speech Processing Using Discrete Speech Unit
Challenge. Notably, we achieved 1st rank on the leaderboard in the TTS track
both with the whole training set and only 1h training data, along with the
lowest bitrate among all submissions.

本文介绍了上海交通大学 X-LANCE 小组在 Interspeech 2024 离散语音单元挑战赛的 TTS（声学 + vocoder）、SVS 和 ASR 任务中开发的系统，尤其是在 TTS 任务中，无论是使用整个训练集还是仅 1 小时训练数据，我们在排行榜中均获得了第一名，且比其他所有提交的系统具有最低比特率。