uture wireless multimedia terminals will have a variety of applications that require speech recognition capabilities. In this paper, we consider a robust distributed speech recognition system where representative parameters of the speech signal are extracted at the wireless terminal and transmitted to a centralized automatic speech recognition (ASR) server. We propose several unequal error prot...

Recently, ASR (Automatic Speech Recognition) functions have commercially been used for various consumer applications including car navigation systems. However, many technical and usability problems still exist before ASR applications are on real business use. Our goal is to make ASR technologies for a real business use. To do so, we first evaluate a car navigation interface which has ASR as an ...

We propose a novel framework for noise robust automatic speech recognition (ASR) based on cochlear implant-like spectrally reduced speech (SRS). Two experimental protocols (EPs) are proposed in order to clarify the advantage of using SRS for noise robust ASR. These two EPs assess the SRS in both the training and testing environments. Speech enhancement was used in one of two EPs to improve the ...

This study evaluates the effect of limestone mineral addition in cement on efficacy supplementary cementitious materials (SCMs) mitigating alkali-silica reaction (ASR) using accelerated mortar bar test (AMBT). Mortars with and without SCMs were prepared by substituting portions 0% general portland (GP) increasing amounts limestone. (25% fly ash or 65% slag) exhibit negligible expansion regardle...

Since users of spoken dialogue systems have difficulty correcting system misconceptions, it is important for automatic speech recognition (ASR) systems to know when their best hypothesis is incorrect. We compare results of previous experiments which showed that prosody improves the detection of ASR errors to experiments with a new system and new domain, the W99 conference registration system. O...

A comparison between automatic speech recognition (ASR) and human speech recognition (HSR) is performed as prerequisite for identifying sources of errors and improving feature extraction in ASR. HSR and ASR experiments are carried out with the same logatome database which consists of nonsense syllables. Two different kinds of signals are presented to human listeners: First, noisy speech samples...

This paper investigates the use of perceptually-motivated subband temporal envelope (STE) features and time-delay neural network (TDNN) denoising autoencoder (DAE) to improve deep neural network (DNN)-based automatic speech recognition (ASR). STEs are estimated by full-wave rectification and low-pass filtering of band-passed speech using a Gammatone filter-bank. TDNNs are used either as DAE or ...

In this study, a fair comparison of human and machine speech recognition is established by using the same paradigms for human speech recognition (HSR) and automatic speech recognition (ASR). In order to ensure equal conditions, a speech database specifically designed for this task is used. The results for HSR and ASR are broken down into several intrinsic variabilities like speaking rate, speak...