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Found 8 papers, 4 papers with code
SALSA: Speedy ASR-LLM Synchronous Aggregation
A challenge of our proposed coupling is handling the mismatch between the tokenizers of the LLM and ASR systems.
Soft Random Sampling: A Theoretical and Empirical Analysis
Soft random sampling (SRS) is a simple yet effective approach for efficient training of large-scale deep neural networks when dealing with massive data.
Improving RNN-Transducers with Acoustic LookAhead
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.
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training
Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost.
Adaptive Discounting of Implicit Language Models in RNN-Transducers
RNN-Transducer (RNN-T) models have become synonymous with streaming end-to-end ASR systems.
Representation based meta-learning for few-shot spoken intent recognition
Spoken intent detection has become a popular approach to interface with various smart devices with ease.
Multilingual and code-switching ASR challenges for low resource Indian languages
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.
Unified Semantic Parsing with Weak Supervision
Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains.
