Samuel Thomas

Training Spoken Language Understanding Systems with Non-Parallel Speech and Text

2020

End-to-end spoken language understanding (SLU) systems are typically trained on large amounts of data. In many practical scenarios, the amount of labeled speech is often limited as opposed to text. In this study, we investigate …

Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems

2020 · arXiv (Cornell University)

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 time consuming and expensive to collect. Initializing the S2I model with …

RNN Transducer Models for Spoken Language Understanding

2021

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 three practical settings: a case where verbatim transcripts are available, …