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Found 3 papers, 0 papers with code
Back from the future: bidirectional CTC decoding using future information in speech recognition
In this paper, we propose a simple but effective method to decode the output of Connectionist Temporal Classifier (CTC) model using a bi-directional neural language model.
Spell my name: keyword boosted speech recognition
Recognition of uncommon words such as names and technical terminology is important to understanding conversations in context.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Continuous Semantic Topic Embedding Model Using Variational Autoencoder
This paper proposes the continuous semantic topic embedding model (CSTEM) which finds latent topic variables in documents using continuous semantic distance function between the topics and the words by means of the variational autoencoder(VAE).
