7 accepted papers
An Xception model reaches state-of-the-art (SOTA) accuracy on the ESC-50 dataset for audio event detection through knowledge transfer from ImageNet weights, pretraining on AudioSet, and an on-the-fly data augmentation pipeline. This paper presents an ablation study that analyzes which components con…
In the presence of a wide variety of dialects, training dialect-specific models for each dialect is a demanding task. Previous studies have explored training a single model that is robust across multiple dialects. These studies have used either multi-condition training, multi-task learning, end-to-e…
Acoustic model combination (AMOC) is an active research area. Model combination techniques are critical for many automatic speech recognition (ASR) scenarios, and provide frameworks to combine diverse acoustic models to boost ASR performance. We scope this work in the broad framework of AMOC, and pr…
Confidence classifier is an integral component of an automatic speech recognition (ASR) system. These classifiers predict the accuracy of an ASR hypothesis by associating a confidence score in [0,1] range, where larger score implies higher probability of the hypothesis being correct. Confidence scor…
Recently, the low-rank plus diagonal (LRPD) adaptation was proposed for speaker adaptation of deep neural network (DNN) models. The LRPD restructures the adaptation matrix as a superposition of a diagonal matrix and a product of two low-rank matrices. In this paper, we extend the LRPD adaptation int…
Recently Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) acoustic models have demonstrated superior performance over deep neural networks (DNN) models in speech recognition and many other tasks. Although a lot of work have been reported on DNN model adaptation, very little has been don…
Previously we demonstrated that speaker adaptation of acoustic models (AM) can provide significant improvement in the accuracy of large-scale speech recognition systems. In this work we discuss numerous challenges in scaling speaker adaptation to millions of speakers, where the size of speaker-depen…