Michael Picheny

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 …

Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings

2021 · arXiv (Cornell University)

Speech recognition models often obtain degraded performance when tested on speech with unseen accents. Domain-adversarial training (DAT) and multi-task learning (MTL) are two common approaches for building accent-robust ASR models. ASR models using accent embeddings …