Search Results for author:
Found 15 papers, 1 papers with code
Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study
In the era of large models, the autoregressive nature of decoding often results in latency serving as a significant bottleneck.
Large-scale Language Model Rescoring on Long-form Data
In this work, we study the impact of Large-scale Language Models (LLM) on Automated Speech Recognition (ASR) of YouTube videos, which we use as a source for long-form ASR.
Alignment Entropy Regularization
Existing training criteria in automatic speech recognition(ASR) permit the model to freely explore more than one time alignments between the feature and label sequences.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
E2E Segmentation in a Two-Pass Cascaded Encoder ASR Model
We explore unifying a neural segmenter with two-pass cascaded encoder ASR into a single model.
Global Normalization for Streaming Speech Recognition in a Modular Framework
We introduce the Globally Normalized Autoregressive Transducer (GNAT) for addressing the label bias problem in streaming speech recognition.
E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR
Improving the performance of end-to-end ASR models on long utterances ranging from minutes to hours in length is an ongoing challenge in speech recognition.
A* shortest string decoding for non-idempotent semirings
We describe an algorithm which finds the shortest string for a weighted non-deterministic automaton over such semirings using the backwards shortest distance of an equivalent deterministic automaton (DFA) as a heuristic for A* search performed over a companion idempotent semiring, which is proven to return the shortest string.
Hybrid Autoregressive Transducer (hat)
This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoderdecoder model that preserves the modularity of conventional automatic speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Federated Learning of N-gram Language Models
The n-gram language models trained with federated learning are compared to n-grams trained with traditional server-based algorithms using A/B tests on tens of millions of users of virtual keyboard.
On the Compression of Lexicon Transducers
Indeed, a delicate balance between comprehensiveness, speed, and memory must be struck to conform to device requirements while providing a good user experience. In this paper, we describe a compression scheme for lexicons when represented as finite-state transducers.
