Language modeling is the task of predicting the next word or character in a document.
( Image credit: Exploring the Limits of Language Modeling )
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In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a self-attentive simple recurrent unit (SRU) for language modeling.
After that, we apply the best norm-scaling setup in combination with various margins and conduct neural language models rescoring experiments in automatic speech recognition.
This is compared to a global renormalization scheme which is equivalent to applying shallow fusion in training.
Here, we propose a conceptually simple method for training instruction-following agents with deep RL that are robust to natural human instructions.
Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks.
Proper nouns present a challenge for end-to-end (E2E) automatic speech recognition (ASR) systems in that a particular name may appear only rarely during training, and may have a pronunciation similar to that of a more common word.
In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance.
This paper describes the NTNU ASR system participating in the Interspeech 2020 Non-Native Children's Speech ASR Challenge supported by the SIG-CHILD group of ISCA.