5 citations · 7 across the 4 of their papers we have counts for
9 papers
Towards a Common Speech Analysis Engine
Hagai Aronowitz, Itai Gat, Edmilson Morais +2
Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-super…
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets
Zvi Kons, Aharon Satt, Hong-Kwang Kuo +4
Intent classifiers are vital to the successful operation of virtual agent systems. This is especially so in voice activated systems where the data can be noisy with many ambiguous…
Speech Emotion Recognition using Self-Supervised Features
Edmilson Morais, Ron Hoory, Weizhong Zhu +3
Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speec…
RNN Transducer Models For Spoken Language Understanding
Samuel Thomas, Hong-Kwang J. Kuo, George Saon +5
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding(SLU). These end-to-end (E2E) models are constructed in thr…
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems
Yinghui Huang, Hong-Kwang Kuo, Samuel Thomas +5
Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is…
End-to-End Spoken Language Understanding Without Full Transcripts
Hong-Kwang J. Kuo, Zoltán Tüske, Samuel Thomas +7
An essential component of spoken language understanding (SLU) is slot filling: representing the meaning of a spoken utterance using semantic entity labels. In this paper, we develo…