Search Results for author:
Found 3 papers, 0 papers with code
Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR
Recently Deep Transformer models have proven to be particularly powerful in language modeling tasks for ASR.
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech
In our recent work we have significantly improved the online performance of a conversational speech transcription system by transferring knowledge from a Recurrent Neural Network Language Model (RNNLM) to the single pass BNLM with text generation based data augmentation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Investigation on N-gram Approximated RNNLMs for Recognition of Morphologically Rich Speech
Recognition of Hungarian conversational telephone speech is challenging due to the informal style and morphological richness of the language.
