Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition

no code implementations • 17 Oct 2023 • Hillary Ngai, Rohan Agrawal, Neeraj Gaur, Ronny Huang, Parisa Haghani, Pedro Moreno Mengibar

Adapters are an efficient, composable alternative to full fine-tuning of pre-trained models and help scale the deployment of large ASR models to many tasks.

speech-recognition Speech Recognition

Improving Rare Word Recognition with LM-aware MWER Training

no code implementations • 15 Apr 2022 • Weiran Wang, Tongzhou Chen, Tara N. Sainath, Ehsan Variani, Rohit Prabhavalkar, Ronny Huang, Bhuvana Ramabhadran, Neeraj Gaur, Sepand Mavandadi, Cal Peyser, Trevor Strohman, Yanzhang He, David Rybach

Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the rescoring setups.

From Audio to Semantics: Approaches to end-to-end spoken language understanding

no code implementations • 24 Sep 2018 • Parisa Haghani, Arun Narayanan, Michiel Bacchiani, Galen Chuang, Neeraj Gaur, Pedro Moreno, Rohit Prabhavalkar, Zhongdi Qu, Austin Waters

Conventional spoken language understanding systems consist of two main components: an automatic speech recognition module that converts audio to a transcript, and a natural language understanding module that transforms the resulting text (or top N hypotheses) into a set of domains, intents, and arguments.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3