We propose a novel context-dependent (CD) model for large-vocabulary speech recognition
(LVSR) that leverages recent advances in using deep belief networks for phone recognition …

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad
range of classification problems. Their generative properties allow better understanding of …

Voice search is the technology underlying many spoken dialog systems (SDSs) that provide
users with the information they request with a spoken query. The information normally exists …

Bottleneck features have been shown to be effective in improving the accuracy of automatic
speech recognition (ASR) systems. Conventionally, bottleneck features are extracted from a …

Recently, deep learning techniques have been successfully applied to automatic speech
recognition tasks--first to phonetic recognition with context-independent deep belief network …

The context-independent deep belief network (DBN) hidden Markov model (HMM) hybrid
architecture has recently achieved promising results for phone recognition. In this work, we …

The growing popularity of mobile search and the advancement in voice recognition
technologies have opened the door for web search users to speak their queries, rather than …

A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) has driven
tremendous improvements on an acoustic model based on Gaussian Mixture Model (GMM) …

This paper proposes a segmental conditional random field framework for large vocabulary
continuous speech recognition. Fundamental to this approach is the use of acoustic …

Most speech recognition applications in use today rely heavily on confidence measure for
making optimal decisions. In this paper, we aim to answer the question: what can be done to …