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Found 10 papers, 1 papers with code
Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition
Prior approaches typically relied on subword encoders for encoding the bias phrases.
Dialog act guided contextual adapter for personalized speech recognition
Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both -- the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing.
Contextual Adapters for Personalized Speech Recognition in Neural Transducers
Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding
In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio.
Natural Language Understanding
Spoken Language Understanding
Extreme Model Compression for On-device Natural Language Understanding
In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices.
Statistical Model Compression for Small-Footprint Natural Language Understanding
In this paper we investigate statistical model compression applied to natural language understanding (NLU) models.
Identifying the Provision of Choices in Privacy Policy Text
Our techniques enable the creation of systems to help Internet users to learn about their choices, thereby effectuating notice and choice and improving Internet privacy.
Gated-Attention Architectures for Task-Oriented Language Grounding
To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment.
