Contextual Speech Recognition with Difficult Negative Training Examples
This work addresses the challenge of accurately recognizing proper nouns in speech recognition, which is incremental as it builds on existing contextual ASR models.
The paper tackled the problem of improving contextual information representation in end-to-end automatic speech recognition by training with difficult negative examples, resulting in up to 53.1% relative improvement in word error rate across benchmarks.
Improving the representation of contextual information is key to unlocking the potential of end-to-end (E2E) automatic speech recognition (ASR). In this work, we present a novel and simple approach for training an ASR context mechanism with difficult negative examples. The main idea is to focus on proper nouns (e.g., unique entities such as names of people and places) in the reference transcript, and use phonetically similar phrases as negative examples, encouraging the neural model to learn more discriminative representations. We apply our approach to an end-to-end contextual ASR model that jointly learns to transcribe and select the correct context items, and show that our proposed method gives up to $53.1\%$ relative improvement in word error rate (WER) across several benchmarks.