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Speech-language pre-training for end-to-end spoken language understanding
End-to-end (E2E) spoken language understanding (SLU) can infer semantics directly from
speech signal without cascading an automatic speech recognizer (ASR) with a natural …
speech signal without cascading an automatic speech recognizer (ASR) with a natural …
Integration of pre-trained networks with continuous token interface for end-to-end spoken language understanding
Most End-to-End (E2E) Spoken Language Understanding (SLU) networks leverage the pre-
trained Automatic Speech Recognition (ASR) networks but still lack the capability to …
trained Automatic Speech Recognition (ASR) networks but still lack the capability to …
Towards reducing the need for speech training data to build spoken language understanding systems
The lack of speech data annotated with labels required for spoken language understanding
(SLU) is often a major hurdle in building end-to-end (E2E) systems that can directly process …
(SLU) is often a major hurdle in building end-to-end (E2E) systems that can directly process …
Zero-shot end-to-end spoken language understanding via cross-modal selective self-training
End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of
collecting speech-semantics pairs, especially when label domains change. Hence, we …
collecting speech-semantics pairs, especially when label domains change. Hence, we …
On the use of semantically-aligned speech representations for spoken language understanding
In this paper we examine the use of semantically-aligned speech representations for end-to-
end spoken language understanding (SLU). We employ the recently-introduced SAMU …
end spoken language understanding (SLU). We employ the recently-introduced SAMU …
End-to-end model for named entity recognition from speech without paired training data
Recent works showed that end-to-end neural approaches tend to become very popular for
spoken language understanding (SLU). Through the term end-to-end, one considers the use …
spoken language understanding (SLU). Through the term end-to-end, one considers the use …
Improving end-to-end speech-to-intent classification with reptile
End-to-end spoken language understanding (SLU) systems have many advantages over
conventional pipeline systems, but collecting in-domain speech data to train an end-to-end …
conventional pipeline systems, but collecting in-domain speech data to train an end-to-end …
Exploring transfer learning for end-to-end spoken language understanding
Abstract Voice Assistants such as Alexa, Siri, and Google Assistant typically use a two-stage
Spoken Language Understanding pipeline; first, an Automatic Speech Recognition (ASR) …
Spoken Language Understanding pipeline; first, an Automatic Speech Recognition (ASR) …
Improving end-to-end speech processing by efficient text data utilization with latent synthesis
Training a high performance end-to-end speech (E2E) processing model requires an
enormous amount of labeled speech data, especially in the era of data-centric artificial …
enormous amount of labeled speech data, especially in the era of data-centric artificial …
Leveraging acoustic and linguistic embeddings from pretrained speech and language models for intent classification
Intent classification is a task in spoken language understanding. An intent classification
system is usually implemented as a pipeline process, with a speech recognition module …
system is usually implemented as a pipeline process, with a speech recognition module …