Transformer-based language modeling and decoding for conversational speech recognition

• URL: http://arxiv.org/abs/2001.01140v1
• Date: Sat, 4 Jan 2020 23:27:59 GMT
• Title: Transformer-based language modeling and decoding for conversational speech recognition
• Authors: Kareem Nassar
• Abstract summary: We focus on decoding efficiently in a weighted finite-state transducer framework. We showcase an approach to lattice re-scoring that allows for longer range history captured by a transfomer-based language model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a way to use a transformer-based language model in conversational speech recognition. Specifically, we focus on decoding efficiently in a weighted finite-state transducer framework. We showcase an approach to lattice re-scoring that allows for longer range history captured by a transfomer-based language model and takes advantage of a transformer's ability to avoid computing sequentially.

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