In this paper, we illustrate the close parallels between the research fields of human speech recognition (HSR) and automatic speech recognition (ASR) using a computational model of human word recognition, SpeM, which was built using techniques from ASR. We show that ASR has proven to be useful for improving models of HSR by relieving them of some of their shortcomings. However, in order to buil...

Unsupervised acoustic modeling can offer a cost and time effective way of creating a solid acoustic model for any under-resourced language. This paper explores the novel idea of using two independent ASR systems to transcribe new speech data, align and filter the ASR hypotheses and use the presumably correct transcriptions to iteratively improve the two seed ASR systems. In parallel, the newly ...

Word neighborhoods have been suggested but not thoroughly explored as an explanatory variable for errors in automatic speech recognition (ASR). We revisit the definition of word neighborhoods, propose new measures using a fine-grained articulatory representation of word pronunciations, and consider new neighbor weighting functions. We analyze the significance of our measures as predictors of er...

Modeling of individual users is a promising way of improving the performance of spoken dialogue systems deployed for the general public and utilized repeatedly. We define “implicitly-supervised” ASR accuracy per user on the basis of responses following the system’s explicit confirmations. We combine the estimated ASR accuracy with the user’s barge-in rate, which represents how well the user is ...

Background and purpose - Blood metal ion levels can be an indicator for detecting implant failure in metal-on-metal (MoM) hip arthroplasties. Little is known about the effect of bilateral MoM implants on metal ion levels and patient-reported outcomes. We compared unilateral patients and bilateral patients with either an ASR hip resurfacing (HR) or an ASR XL total hip replacement (THR) and inves...

This paper is concerned with the implementation of automatic speech recognition (ASR) based services on wireless mobile devices. Techniques are investigated for improving the performance of ASR systems in the context of the devices themselves, the environments that they are used in, and the networks they are connected to. A set of ASR tasks and ASR system architectures that are applicable to a ...

We establish a framework for studying the behavior of automatic speech recognition (ASR) lattices (viewed as automata) undergoing determinization. Using this framework, we provide initial insights into what causes determinization to produce smaller (or bigger) lattices when used in the ASR application. Our results counter the prevailing wisdom that the graph topology underlying an automaton, no...

Visual speech information is known to improve accuracy and noise robustness of automatic speech recognizers. However, todate, all audio-visual ASR work has concentrated on “visually clean” data with limited variation in the speaker’s frontal pose, lighting, and background. In this paper, we investigate audiovisual ASR in two practical environments that present significant challenges to robust v...

Previous use of gaze (eye movement) to improve ASR performance involves shifting language model probability mass towards the subset of the vocabulary whose words are related to a person’s visual attention. Motivated to improve Automatic Speech Recognition (ASR) performance in acoustically noisy settings by using information from gaze selectively, we propose a ‘Selective Gaze-contingent ASR’ (SG...

A challenge in large vocabulary spoken language understanding (SLU) is robustness to automatic speech recognition (ASR) errors. The state of the art approaches for semantic parsing rely on using discriminative sequence classification methods, such as conditional random fields (CRFs). Most dialog systems employ a cascaded approach where the best hypotheses from the ASR system are fed into the fo...