9 citations · 32 across the 22 of their papers we have counts for
4 papers · 1 filter
Tie Your Embeddings Down: Cross-Modal Latent Spaces for End-to-end Spoken Language Understanding
Bhuvan Agrawal, Markus Müller, Martin Radfar +3
End-to-end (E2E) spoken language understanding (SLU) systems can infer the semantics of a spoken utterance directly from an audio signal. However, training an E2E system remains a…
End-to-End Neural Transformer Based Spoken Language Understanding
Martin Radfar, Athanasios Mouchtaris, Siegfried Kunzmann
Spoken language understanding (SLU) refers to the process of inferring the semantic information from audio signals. While the neural transformers consistently deliver the best perf…
Semantic Complexity in End-to-End Spoken Language Understanding
Joseph P. McKenna, Samridhi Choudhary, Michael Saxon +2
End-to-end spoken language understanding (SLU) models are a class of model architectures that predict semantics directly from speech. Because of their input and output types, we re…
Streaming End-to-End Bilingual ASR Systems with Joint Language Identification
Surabhi Punjabi, Harish Arsikere, Zeynab Raeesy +11
Multilingual ASR technology simplifies model training and deployment, but its accuracy is known to depend on the availability of language information at runtime. Since language ide…