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.

Feature Extraction for Classification in the Data Mining Process

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of “the curse of dimensionality”. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.

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.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

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.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Does cochlear implantation improve speech recognition in children with auditory neuropathy spectrum disorder? A systematic review

OBJECTIVE: Cochlear implantation (CI) is a standard treatment for severe-profound sensorineural hearing loss (SNHL). However, consensus has yet to be reached on its effectiveness for hearing loss caused by auditory neuropathy spectrum disorder (ANSD). This review aims to summarize and synthesize current evidence of the effectiveness of CI in improving speech recognition in children with ANSD. DESIGN: Systematic review. STUDY SAMPLE: A total of 27 studies from an initial selection of 237. RESULTS: All selected studies were observational in design, including case studies, cohort studies, and comparisons between children with ANSD and SNHL. Most children with ANSD achieved open-set speech recognition with their CI. Speech recognition ability was found to be equivalent in CI users (who previously performed poorly with hearing aids) and hearing-aid users. Outcomes following CI generally appeared similar in children with ANSD and SNHL. Assessment of study quality, however, suggested substantial methodological concerns, particularly in relation to issues of bias and confounding, limiting the robustness of any conclusions around effectiveness. CONCLUSIONS: Currently available evidence is compatible with favourable outcomes from CI in children with ANSD. However, this evidence is weak. Stronger evidence is needed to support cost-effective clinical policy and practice in this area.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

Conversação homem-máquina. Caracterização e avaliação do estado actual das soluções de speech recognition, speech synthesis e sistemas de conversação homem-máquina

A comunicação verbal humana é realizada em dois sentidos, existindo uma compreensão de ambas as partes que resulta em determinadas considerações. Este tipo de comunicação, também chamada de diálogo, para além de agentes humanos pode ser constituído por agentes humanos e máquinas. A interação entre o Homem e máquinas, através de linguagem natural, desempenha um papel importante na melhoria da comunicação entre ambos. Com o objetivo de perceber melhor a comunicação entre Homem e máquina este documento apresenta vários conhecimentos sobre sistemas de conversação Homemmáquina, entre os quais, os seus módulos e funcionamento, estratégias de diálogo e desafios a ter em conta na sua implementação. Para além disso, são ainda apresentados vários sistemas de Speech Recognition, Speech Synthesis e sistemas que usam conversação Homem-máquina. Por último são feitos testes de performance sobre alguns sistemas de Speech Recognition e de forma a colocar em prática alguns conceitos apresentados neste trabalho, é apresentado a implementação de um sistema de conversação Homem-máquina. Sobre este trabalho várias ilações foram obtidas, entre as quais, a alta complexidade dos sistemas de conversação Homem-máquina, a baixa performance no reconhecimento de voz em ambientes com ruído e as barreiras que se podem encontrar na implementação destes sistemas.

Perception of co-speech gestures in aphasic patients: A visual exploration study during the observation of dyadic conversations.

BACKGROUND Co-speech gestures are part of nonverbal communication during conversations. They either support the verbal message or provide the interlocutor with additional information. Furthermore, they prompt as nonverbal cues the cooperative process of turn taking. In the present study, we investigated the influence of co-speech gestures on the perception of dyadic dialogue in aphasic patients. In particular, we analysed the impact of co-speech gestures on gaze direction (towards speaker or listener) and fixation of body parts. We hypothesized that aphasic patients, who are restricted in verbal comprehension, adapt their visual exploration strategies. METHODS Sixteen aphasic patients and 23 healthy control subjects participated in the study. Visual exploration behaviour was measured by means of a contact-free infrared eye-tracker while subjects were watching videos depicting spontaneous dialogues between two individuals. Cumulative fixation duration and mean fixation duration were calculated for the factors co-speech gesture (present and absent), gaze direction (to the speaker or to the listener), and region of interest (ROI), including hands, face, and body. RESULTS Both aphasic patients and healthy controls mainly fixated the speaker's face. We found a significant co-speech gesture × ROI interaction, indicating that the presence of a co-speech gesture encouraged subjects to look at the speaker. Further, there was a significant gaze direction × ROI × group interaction revealing that aphasic patients showed reduced cumulative fixation duration on the speaker's face compared to healthy controls. CONCLUSION Co-speech gestures guide the observer's attention towards the speaker, the source of semantic input. It is discussed whether an underlying semantic processing deficit or a deficit to integrate audio-visual information may cause aphasic patients to explore less the speaker's face.

Perception of co-speech gestures in aphasic patients: A visual exploration study during the observation of dyadic conversations

Background: Co-speech gestures are part of nonverbal communication during conversations. They either support the verbal message or provide the interlocutor with additional information. Furthermore, they prompt as nonverbal cues the cooperative process of turn taking. In the present study, we investigated the influence of co-speech gestures on the perception of dyadic dialogue in aphasic patients. In particular, we analysed the impact of co-speech gestures on gaze direction (towards speaker or listener) and fixation of body parts. We hypothesized that aphasic patients, who are restricted in verbal comprehension, adapt their visual exploration strategies. Methods: Sixteen aphasic patients and 23 healthy control subjects participated in the study. Visual exploration behaviour was measured by means of a contact-free infrared eye-tracker while subjects were watching videos depicting spontaneous dialogues between two individuals. Cumulative fixation duration and mean fixation duration were calculated for the factors co-speech gesture (present and absent), gaze direction (to the speaker or to the listener), and region of interest (ROI), including hands, face, and body. Results: Both aphasic patients and healthy controls mainly fixated the speaker’s face. We found a significant co-speech gesture x ROI interaction, indicating that the presence of a co-speech gesture encouraged subjects to look at the speaker. Further, there was a significant gaze direction x ROI x group interaction revealing that aphasic patients showed reduced cumulative fixation duration on the speaker’s face compared to healthy controls. Conclusion: Co-speech gestures guide the observer’s attention towards the speaker, the source of semantic input. It is discussed whether an underlying semantic processing deficit or a deficit to integrate audio-visual information may cause aphasic patients to explore less the speaker’s face. Keywords: Gestures, visual exploration, dialogue, aphasia, apraxia, eye movements

Visual-auditory integration during speech imitation in autism

Children with autistic spectrum disorder (ASD) may have poor audio-visual integration, possibly reflecting dysfunctional 'mirror neuron' systems which have been hypothesised to be at the core of the condition. In the present study, a computer program, utilizing speech synthesizer software and a 'virtual' head (Baldi), delivered speech stimuli for identification in auditory, visual or bimodal conditions. Children with ASD were poorer than controls at recognizing stimuli in the unimodal conditions, but once performance on this measure was controlled for, no group difference was found in the bimodal condition. A group of participants with ASD were also trained to develop their speech-reading ability. Training improved visual accuracy and this also improved the children's ability to utilize visual information in their processing of speech. Overall results were compared to predictions from mathematical models based on integration and non-integration, and were most consistent with the integration model. We conclude that, whilst they are less accurate in recognizing stimuli in the unimodal condition, children with ASD show normal integration of visual and auditory speech stimuli. Given that training in recognition of visual speech was effective, children with ASD may benefit from multi-modal approaches in imitative therapy and language training. (C) 2004 Elsevier Ltd. All rights reserved.

Auditory, visual, and auditory-visual recognition of vowels by hearing-impaired children

This paper studies the auditory, visual and combined audio-visual recognition of vowels by severely and profoundly hearing impaired children.