1 citations · 2 across the 14 of their papers we have counts for
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Multi-Modal Retrieval For Large Language Model Based Speech Recognition
Jari Kolehmainen, Aditya Gourav, Prashanth Gurunath Shivakumar +5
Retrieval is a widely adopted approach for improving language models leveraging external information. As the field moves towards multi-modal large language models, it is important…
Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition
Xuandi Fu, Kanthashree Mysore Sathyendra, Ankur Gandhe +4
Attention-based contextual biasing approaches have shown significant improvements in the recognition of generic and/or personal rare-words in End-to-End Automatic Speech Recognitio…
Dialog act guided contextual adapter for personalized speech recognition
Feng-Ju Chang, Thejaswi Muniyappa, Kanthashree Mysore Sathyendra +3
Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackle…
Contextual Adapters for Personalized Speech Recognition in Neural Transducers
Kanthashree Mysore Sathyendra, Thejaswi Muniyappa, Feng-Ju Chang +5
Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data. A standard way to address this issue is…
A neural prosody encoder for end-ro-end dialogue act classification
Kai Wei, Dillon Knox, Martin Radfar +6
Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems. Prosodic features such as energy and pitch have been shown to be useful…
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding
Xuandi Fu, Feng-Ju Chang, Martin Radfar +4
End-to-end Spoken Language Understanding (E2E SLU) has attracted increasing interest due to its advantages of joint optimization and low latency when compared to traditionally casc…