4 accepted papers
Self-supervised models, such as HuBERT and its audio-visual version AV-HuBERT, have demonstrated excellent performance on various tasks. The main factor for their success is the pre-training procedure, which requires only raw data without human transcription. During the self-supervised pre-training…
This paper reports on investigations using two techniques for language model text data augmentation for low-resourced automatic speech recognition and keyword search. Lowresourced languages are characterized by limited training materials, which typically results in high out-of-vocabulary (OOV) rates…
In this paper we aim to enhance keyword search for conversational telephone speech under low-resourced conditions. Two techniques to improve the detection of out-of-vocabulary keywords are assessed in this study: using extra text resources to augment the lexicon and language model, and via subword u…
This paper presents a method to improve a language model for a limited-resourced language using statistical machine translation from a related language to generate data for the target language. In this work, the machine translation model is trained on a corpus of parallel Mandarin-Cantonese subtitle…