Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Thesis (Ph.D.)--University of Washington, 2016-06

Attentional shifting and the role of the dorsal pathway in visual word recognition

A substantial amount of evidence has been collected to propose an exclusive role for the dorsal visual pathway in the control of guided visual search mechanisms, specifically in the preattentive direction of spatial selection [Vidyasagar, T. R. (1999). A neuronal model of attentional spotlight: Parietal guiding the temporal. Brain Research and Reviews, 30, 66-76; Vidyasagar, T. R. (2001). From attentional gating in macaque primary visual cortex to dyslexia in humans. Progress in Brain Research, 134, 297-312]. Moreover, it has been suggested recently that the dorsal visual pathway is specifically involved in the spatial selection and sequencing required for orthographic processing in visual word recognition. In this experiment we manipulate the demands for spatial processing in a word recognition, lexical decision task by presenting target words in a normal spatial configuration, or where the constituent letters of each word are spatially shifted relative to each other. Accurate word recognition in the Shifted-words condition should demand higher spatial encoding requirements, thereby making greater demands on the dorsal visual stream. Magnetoencephalographic (MEG) neuroimaging revealed a high frequency (35-40 Hz) right posterior parietal activation consistent with dorsal stream involvement occurring between 100 and 300 ms post-stimulus onset, and then again at 200-400 ms. Moreover, this signal was stronger in the shifted word condition, compared to the normal word condition. This result provides neurophysiological evidence that the dorsal visual stream may play an important role in visual word recognition and reading. These results further provide a plausible link between early stage theories of reading, and the magnocellular-deficit theory of dyslexia, which characterises many types of reading difficulty. © 2006 Elsevier Ltd. All rights reserved.

Visual word recognition:The first half second

We used magnetoencephalography (MEG) to map the spatiotemporal evolution of cortical activity for visual word recognition. We show that for five-letter words, activity in the left hemisphere (LH) fusiform gyrus expands systematically in both the posterior-anterior and medial-lateral directions over the course of the first 500 ms after stimulus presentation. Contrary to what would be expected from cognitive models and hemodynamic studies, the component of this activity that spatially coincides with the visual word form area (VWFA) is not active until around 200 ms post-stimulus, and critically, this activity is preceded by and co-active with activity in parts of the inferior frontal gyrus (IFG, BA44/6). The spread of activity in the VWFA for words does not appear in isolation but is co-active in parallel with spread of activity in anterior middle temporal gyrus (aMTG, BA 21 and 38), posterior middle temporal gyrus (pMTG, BA37/39), and IFG. © 2004 Elsevier Inc. All rights reserved.

The human factors of automatic speech recognition in control room systems

This thesis addresses the viability of automatic speech recognition for control room systems; with careful system design, automatic speech recognition (ASR) devices can be useful means for human computer interaction in specific types of task. These tasks can be defined as complex verbal activities, such as command and control, and can be paired with spatial tasks, such as monitoring, without detriment. It is suggested that ASR use be confined to routine plant operation, as opposed the critical incidents, due to possible problems of stress on the operators' speech.  It is proposed that using ASR will require operators to adapt a commonly used skill to cater for a novel use of speech. Before using the ASR device, new operators will require some form of training. It is shown that a demonstration by an experienced user of the device can lead to superior performance than instructions. Thus, a relatively cheap and very efficient form of operator training can be supplied by demonstration by experienced ASR operators. From a series of studies into speech based interaction with computers, it is concluded that the interaction be designed to capitalise upon the tendency of operators to use short, succinct, task specific styles of speech. From studies comparing different types of feedback, it is concluded that operators be given screen based feedback, rather than auditory feedback, for control room operation. Feedback will take two forms: the use of the ASR device will require recognition feedback, which will be best supplied using text; the performance of a process control task will require task feedback integrated into the mimic display. This latter feedback can be either textual or symbolic, but it is suggested that symbolic feedback will be more beneficial. Related to both interaction style and feedback is the issue of handling recognition errors. These should be corrected by simple command repetition practices, rather than use error handling dialogues. This method of error correction is held to be non intrusive to primary command and control operations. This thesis also addresses some of the problems of user error in ASR use, and provides a number of recommendations for its reduction.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Speech recognition use in healthcare applications

Speech recognition technology is regarded as a key enabler for increasing the usability of applications deployed on mobile devices -- devices which are becoming increasingly prevalent in modern hospital-based healthcare. Although the use of speech recognition is not new to the hospital-based healthcare domain, its use with mobile devices has thus far been limited. This paper presents the results of a literature review we conducted in order to observe the manner in which speech recognition technology has been used in hospital-based healthcare and to gain an understanding of how this technology is being evaluated, in terms of its dependability and reliability, in healthcare settings. Our intent is that this review will help identify scope for future uses of speech recognition technologies in the healthcare domain, as well as to identify implications for the meaningful evaluation of such technologies given the specific context of use.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Speech recognition use in healthcare applications

Speech recognition technology is regarded as a key enabler for increasing the usability of applications deployed on mobile devices -- devices which are becoming increasingly prevalent in modern hospital-based healthcare. Although the use of speech recognition is not new to the hospital-based healthcare domain, its use with mobile devices has thus far been limited. This paper presents the results of a literature review we conducted in order to observe the manner in which speech recognition technology has been used in hospital-based healthcare and to gain an understanding of how this technology is being evaluated, in terms of its dependability and reliability, in healthcare settings. Our intent is that this review will help identify scope for future uses of speech recognition technologies in the healthcare domain, as well as to identify implications for the meaningful evaluation of such technologies given the specific context of use.

Improved Hybrid Model of HMM/GMM for Speech Recognition

In this paper, we propose a speech recognition engine using hybrid model of Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM). Both the models have been trained independently and the respective likelihood values have been considered jointly and input to a decision logic which provides net likelihood as the output. This hybrid model has been compared with the HMM model. Training and testing has been done by using a database of 20 Hindi words spoken by 80 different speakers. Recognition rates achieved by normal HMM are 83.5% and it gets increased to 85% by using the hybrid approach of HMM and GMM.

Re-visualising international relations:audio-visual projects and direct encounters with the political in security studies

In this paper we discuss how an innovative audio-visual project was adopted to foster active, rather than declarative learning, in critical International Relations (IR). First, we explore the aesthetic turn in IR, to contrast this with forms of representation that have dominated IR scholarship. Second, we describe how students were asked to record short audio or video projects to explore their own insights through aesthetic and non-written formats. Third, we explain how these projects are understood to be deeply embedded in social science methodologies. We cite our inspiration from applying a personal sociological imagination, as a way to counterbalance a ‘marketised’ slant in higher education, in a global economy where students are often encouraged to consume, rather than produce knowledge. Finally, we draw conclusions in terms of deeper forms of student engagement leading to new ways of thinking and presenting new skills and new connections between theory and practice.

Weighted finite-state transducers in speech recognition : a compaction algorithm for non-determinizable transducers

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Weighted finite-state transducers in speech recognition : a compaction algorithm for non-determinizable transducers

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.