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Found 7 papers, 0 papers with code
TransformerFAM: Feedback attention is working memory
While Transformers have revolutionized deep learning, their quadratic attention complexity hinders their ability to process infinitely long inputs.
Hierarchical Recurrent Adapters for Efficient Multi-Task Adaptation of Large Speech Models
However, their per-task parameter overhead is considered still high when the number of downstream tasks to adapt for is large.
Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition
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.
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm
Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Massive End-to-end Models for Short Search Queries
In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for offline recognition of voice search queries, with up to 2B model parameters.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving Speech Recognition for African American English With Audio Classification
By combining the classifier output with coarse geographic information, we can select a subset of utterances from a large corpus of untranscribed short-form queries for semi-supervised learning at scale.
Non-Parallel Voice Conversion for ASR Augmentation
For ASR augmentation, it is necessary that the VC model be robust to a wide range of input speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
