Soft random sampling (SRS) is a simple yet effective approach for efficient training of large-scale deep neural networks when dealing with massive data.
RNN-Transducers (RNN-Ts) have gained widespread acceptance as an end-to-end model for speech to text conversion because of their high accuracy and streaming capabilities.
Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost.
RNN-Transducer (RNN-T) models have become synonymous with streaming end-to-end ASR systems.
Spoken intent detection has become a popular approach to interface with various smart devices with ease.
1 code implementation • 1 Apr 2021 • Anuj Diwan, Rakesh Vaideeswaran, Sanket Shah, Ankita Singh, Srinivasa Raghavan, Shreya Khare, Vinit Unni, Saurabh Vyas, Akash Rajpuria, Chiranjeevi Yarra, Ashish Mittal, Prasanta Kumar Ghosh, Preethi Jyothi, Kalika Bali, Vivek Seshadri, Sunayana Sitaram, Samarth Bharadwaj, Jai Nanavati, Raoul Nanavati, Karthik Sankaranarayanan, Tejaswi Seeram, Basil Abraham
For this purpose, we provide a total of ~600 hours of transcribed speech data, comprising train and test sets, in these languages including two code-switched language pairs, Hindi-English and Bengali-English.
Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains.