Analysis Of Speech Patterns Research Articles

HEAR-MHE study: Automated speech analysis identifies minimal hepatic encephalopathy and may predict future overt hepatic encephalopathy.

Hepatic encephalopathy (HE) is a major cause of poor quality of life in patients with cirrhosis. A simple diagnostic test to identify minimal HE (MHE) and predict future overt HE (OHE) is lacking. We aimed to evaluate if analysis of speech patterns using a modern speech platform: 1) correlates with validated HE tests, 2) correlates with MHE, and 3) predicts future OHE. In a two-center prospective cohort study of 200 outpatients with cirrhosis and 50 controls, patients underwent baseline speech recording and validated HE diagnostic testing with psychometric HE score (PHES). Patients were followed for 6 months to identify episodes of OHE. 752 speech variables were extracted using an automated speech analysis platform, reflecting the acoustic, lexical, and semantic aspects of speech. Patients with cirrhosis were median 63 years old (IQR 54, 68), 49.5% (99) were female. Over 100 speech variables were significantly associated with PHES (p <0.05 with FDR adjustment). A three-variable speech model (two acoustic, one speech tempo variable) was similar to animal naming test in predicting MHE (AUC 0.76 vs. 0.69; p=0.11). Adding age and MELD-Na improved accuracy of the speech model (AUC: 0.82). A combined clinical-speech model ("HEAR-MHE model") predicted time to OHE with a concordance of 0.74 (p=0.06). Automated speech analysis highly correlated with validated HE tests, associated with MHE, and may predict future OHE. Future research is needed to validate this tool and to understand how it can be implemented in clinical practice.

Voice Recognition Based on Machine Learning Classification Algorithms: A Review

One essential component of biometric identity is voice recognition technology, which uses speech pattern analysis to authenticate people. With an emphasis on machine learning classification techniques, this review article thoroughly examines the field of speech recognition. We examine the effectiveness of random forest (RF), multilayer perceptrons (MLP), k-nearest neighbours (KNN), and support vector machine (SVM) classifiers via painstaking analysis and empirical evaluation. Utilizing a collection of Sepedi speech audio files, our results demonstrate the remarkable accuracy of 99.86% that RF is capable of producing. Aside from visual aids for better understanding, assessment indicators like as accuracy, precision, recall, F-measure, and root mean square error (RMSE) clarify the effectiveness of the model. The research highlights how machine learning algorithms, especially reinforcement learning (RF), have the capacity to revolutionize speech recognition technology in a variety of contexts.

Bloggers’ Speech Behavior: Analysis and Diagnosis of Personal Traits from the Perspective of Implicit and Functional Pragmalinguistics

The aim of this paper is to present a comprehensive methodology for conducting both traditional and modified content analysis using transcripts of spoken language from video bloggers as source material. The specific focus of this research centers around video content dedicated to reviewing retro automotive vehicles. The authors delve into two fundamental branches of pragmalinguistics, namely the implicit and functional aspects, while also detailing the analytical techniques employed within these paradigms. The practical application of these methods is illustrated using the oral speeches of the blogger Alexander Chikunov as a case study, providing a clear and tangible demonstration of the process for diagnosing individual personality traits. The initial phase involves an analysis of the material through the lens of implicit pragmalinguistics, encompassing the frequency analysis of speech patterns within the context of selected linguistic strategies, coupled with rigorous statistical data processing. The outcomes pertaining to a specific blogger are subsequently juxtaposed against data from other bloggers within the same genre, thereby facilitating the identification of shared, stereotypical, and unique personality characteristics attributed to the blogger. Moving forward, this article highlights individual traits of the blogger, which were discerned through traditional content analysis techniques. Finally, the study involves an insightful interpretation of the acquired data, culminating in the development of a nuanced portrait of Alexander Chikunov’s speech behavior. A notable aspect of this research lies in its pioneering effort to discern and systematize linguistic features exclusively found within the domain of video blogs. This is achieved by utilizing unscripted spoken language from bloggers as empirical evidence, adding a distinctive dimension to the study’s scientific novelty.

EMOTION DETECTION USING AUDIO DATA SAMPLES

A person’s speech can be altered by various changes in the autonomic nervous system and effective technologies can process this information to recognize emotion. As an example, speech produced in a state of fear, anger, or joy becomes loud and fast, with a higher and wider range in pitch, whereas emotions such as sadness or tiredness generate slow and low-pitched speech. Detection of human emotions through voice-pattern and speech-pattern analysis has many applications such as better assisting human-machine interactions. This paper aims to detect emotions from audio. Several machine learning algorithms including K-nearest neighbours (KNN) and decision trees were implemented, based on acoustic features such as Mel Frequency Cepstral Coefficient (MFCC). Our evaluation shows that the proposed approach yields accuracies of 98%, 92% and 99% using KNN, Decision Trees and Extra-Tree Classifiers, respectively, for 7 emotions using Toronto Emotional Speech Set (TESS) Dataset.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

BackgroundThe use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined.MethodsIn this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation.ResultsExperimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms.ConclusionsThis study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

A Multiple-Classifier Framework for Parkinson's Disease Detection Based on Various Vocal Tests.

Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson's disease (PD) have attracted many researchers. For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson's disease (PWP). Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD. However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms. Thus, summarizing the vocal tests may lead into loss of valuable information. In order to address this issue, the classification setting must take what has been said into account. As a solution, we introduced a new framework that applies an independent classifier for each vocal test. The final classification result would be a majority vote from all of the classifiers. When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%.

Using automatic speech recognition to identify dementia in early stages

Early non-invasive diagnosis of Alzheimer’s disease (AD) and other forms of dementia is a challenging task. Early detection of the symptoms of the disorder could help families and medical professionals prepare for the difficulties ahead, as well as possibly provide a recruitment tool for clinical trials. One possible approach to a non-invasive diagnosis is based on analysis of speech patterns. Subjects are asked to describe a picture and their description (typically 1 to 3 minute speech sample) is recorded. For this study, a database of 70 people were recorded, 24 with a clinical diagnosis of probable or possible Alzheimer’s disease. When these data were combined with 140 other recorded samples, a classifier built with manually transcribed versions of the speech was found to be quite accurate for determining whether or not a speech sample was obtained from an Alzheimer’s patient. A classifier built using automatically determined prosodic features (pitch and energy contours) was also reasonably accurate, with several subsets of pitch and energy features especially effective for classification, as assessed by cross validation. The manually transcribed text has now been replaced by automatically transcribed text using automatic speech recognition (ASR) technology. The main objective of this paper is to report on the relative effectiveness of several ASR approaches, including public domain ones, for this task.

Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings.

There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.

Speech Pattern Analysis for Four South Indian Languages

The results of an analysis of speech ON/OFF patterns for four major south Indian languages are presented in this paper. The speech activity, mean spurt length and count, and reduction in activity due to elimination of predictable samples have been analyzed using recorded Kannada, Tamil, Telugu and Malayalam telephone conversations. The speech is analysed with a Speech Pattern Analyzer, and the parameters measured were further analyzed on a computer to obtain the statistics. The results are useful in the design of speech based systems like DSI, DCME etc.

The use of diacritics for visual articulatory behaviours

This paper proposes the use of a set of symbols related to the International Phonetic Alphabet (IPA) and diacritics for the description and notation of articulatory behaviours which can be perceived visually, but which have no effect on the perceived auditory quality of phonemes produced. It is suggested that the use of such symbols will clarify clinical analysis of speech patterns. Examples of clinical usage are given.

Cognitive complexity and duration of classroom speech

Analysis of speech patterns in 27 high school discussion classes revealed that both teachers and students, and both males and females, took an increasingly longer time to express successively higher-cognitive types of utterances. Student utterances, though systematically briefer, followed precisely the same pattern as teacher utterances. The results suggest that duration of speech is related to level of cognition.

A microcomputer-based pause analysis apparatus

The study of pauses in human speech is an important component of the peycholinguistic analysis of speech patterns. Since the inception of this type of research, problems in instrumentation have presented a number of concerns. These problems have been well documented in the literature in pausology. While accurate instrumentation has been developed by several research teams, cost of such equipment has been high, and hand recording of data has been required. This report describes a microcomputer-based pause analysis apparatus, which enables the measurement of pause times precise to better than .01 sec at less cost than previous technology. The apparatus consists of analog hardware and digital computer hardware and software. A printout provides a permanent record of pause times keyed on a line-by-line basis to transcripts. Flexibility is built in by means of a BASIC program enabling specifications for selections of pauses between words, phrases, or sentences based on pause duration.

Speech style and impression formation in a court setting: The effects of “powerful” and “powerless” speech

On the basis of a previous empirical analysis of speech patterns in court trials, speech styles were identified that covaried with speaker social status and power. The “powerless” style is characterized by the frequent use of such linguistic features as intensifiers, hedges, hesitation forms, and questioning intonations, whereas the “powerful” style is marked by less frequent use of these features. Male and female introductory psychology students heard the testimony of a male or female witness who used either a powerful or a powerless style to deliver the same substantive evidence. The testimony was presented either on audio tape or in written transcripts. Use of the powerful style resulted in greater attraction to the witness, regardless of the sex of the witness, the sex of the subject, or the mode of presentation of the testimony. The powerful style also resulted in greater perceived credibility of the witness than did the powerless style; however, this effect was stronger when the subject and the witness were of the same sex than when they were of the opposite sex. In all but the male witness-written presentation condition, the powerful style produced more acceptance of the position advocated in the testimony than did the powerless style. The results are discussed with regard to possible relations between speech style and person perception and persuasion processes and with regard to the social psychology of legal issues.

Some innovations in a computer approach to the analysis of speech patterns.

A procedure is presented which contains a number of innovations in the analysis of temporal characteristics of speech patterns by computer. The computer set-up developed permits the duplication of the source tape as it is being analysed, with beep insertions on the duplicated tape at pause locations. A two-step "throwaway" procedure is introduced to eliminate speech bursts not reaching minimum criterion duration. This may ensure greater accuracy than the one-step procedure normally used. In addition, a method is described for substituting "tone" tapes for "live" recordings to overcome some of the problems associated with the automatized analysis of speech, especially of a spontaneous nature. The procedures are described in the context of a study on simultaneous interpretation, which involves two input sources.