Automatic recognition of dysarthric and elderly speech highly challenging
tasks to date. Speaker-level heterogeneity attributed to accent or gender
commonly found in normal speech, when aggregated with age and speech impairment
severity, create large diversity among speakers. Speaker adaptation techniques
play a crucial role in personalization of ASR systems for such users. Their
mobility issues limit the amount of speaker-level data available for model
based adaptation. To this end, this paper investigates two novel forms of
feature based on-the-fly rapid speaker adaptation approaches. The first is
based on speaker-level variance regularized spectral basis embedding (SBEVR)
features, while the other uses on-the-fly learning hidden unit contributions
(LHUC) transforms conditioned on speaker-level spectral features. Experiments
conducted on the UASpeech dysarthric and DimentiaBank Pitt elderly speech
datasets suggest the proposed SBEVR features based adaptation statistically
significantly outperform both the baseline on-the-fly i-Vector adapted hybrid
TDNN/DNN systems by up to 2.48% absolute (7.92% relative) reduction in word
error rate (WER), and offline batch mode model based LHUC adaptation using all
speaker-level data by 0.78% absolute (2.41% relative) in WER reduction.

该研究提出了两种新型的基于特征的即时演讲者适应方法，即基于演讲者水平的方差规范化谱基础嵌入 (SBEVR) 特征和基于即时学习的隐藏单元贡献 (LHUC) 转换。实验结果显示，这两种方法在适应演讲者语音时表现出色，其中基于 SBEVR 特征的适应方式的性能优于基于模型的 LHUC 适应。