Search Results for author:
Found 9 papers, 1 papers with code
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models
We conducted extensive experiments with a 2-billion parameter USM on a large-scale voice search dataset to evaluate our proposed method.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
2-bit Conformer quantization for automatic speech recognition
With the large-scale training data, we obtain a 2-bit Conformer model with over 40% model size reduction against the 4-bit version at the cost of 17% relative word error rate degradation
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models
With the rapid increase in the size of neural networks, model compression has become an important area of research.
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition
As end-to-end automatic speech recognition (ASR) models reach promising performance, various downstream tasks rely on good confidence estimators for these systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Large-scale ASR Domain Adaptation using Self- and Semi-supervised Learning
Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance.
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction
Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Learning Word-Level Confidence For Subword End-to-End ASR
We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition
For various speech-related tasks, confidence scores from a speech recogniser are a useful measure to assess the quality of transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Probabilistic Clustering Using Maximal Matrix Norm Couplings
In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable.
