Despite these successes, speech synthesis and recognition challenges persist, including difficulties
generating natural-sounding speech in non-English languages and real-time speech generation.
To address these limitations, Wang et al. [658] proposed a novel approach that combines noisy
student training with SpecAugment and large Conformer models pre-trained on the Libri-Light
dataset using the wav2vec 2.0 pre-training method. This approach achieved state-of-the-art word
error rates on the LibriSpeech dataset. Recently, Wang et al. [575] developed Conformer-LHUC,
an extension of the Conformer model that employs learning hidden unit contribution (LHUC) for
speaker adaptation. Conformer-LHUC has demonstrated exceptional performance in elderly speech
recognition and shows promise for the clinical diagnosis and treatment of Alzheimer’s disease.
AReviewofDeepLearningTechniquesforSpeechProcessing