Recurrent neural network language models (RNNLMs) have recently become increasingly
popular for many applications including speech recognition. In previous research RNNLMs …

Popular techniques for domain adaptation such as the feature augmentation method of
Daumé III (2009) have mostly been considered for sparse binary-valued features, but not for …

Recognizing written domain numeric utterances (eg I need $1.25.) can be challenging for
ASR systems, particularly when numeric sequences are not seen during training. This out-of …

Recurrent neural network language models (RNNLMs) generally outperform n-gram
language models when used in automatic speech recognition (ASR). Adapting RNNLMs to …

Recurrent neural network language models (RNNLMs) have been shown to consistently
improve Word Error Rates (WERs) of large vocabulary speech recognition systems …

This paper deals with a complex acoustic analysis of phonation in patients with Parkinson's
disease (PD) with a special focus on estimation of disease progress that is described by 7 …

This paper presents methods to accelerate recurrent neural network based language
models (RNNLMs) for online speech recognition systems. Firstly, a lossy compression of the …

Language models (LM) for interactive speech recognition systems are trained on large
amounts of data and the model parameters are optimized on past user data. New …

One particular problem in large vocabulary continuous speech recognition for low-resourced
languages is finding relevant training data for the statistical language models. Large amount …

Recurrent neural network language models (RNNLMs) have consistently outperformed n-
gram language models when used in automatic speech recognition (ASR). This is because …