Over the past several decades, many acoustic markers have been proposed to be sensitive to and measure overall voice quality. This meta-analysis presents a retrospective appraisal of scientific reports, which evaluated the relation between perceived overall voice quality and several acoustic-phonetic correlates. Twenty-five studies met the inclusion criteria and were evaluated using meta-analytic techniques. Correlation coefficients between perceptual judgments and acoustic measures were computed. Where more than one correlation coefficient for a specific acoustic marker was available, a weighted average correlation coefficient was calculated. This was the case in 36 acoustic measures on sustained vowels and in 3 measures on continuous speech. Acoustic measures were ranked according to the strength of the correlation with perceptual voice quality ratings. Acoustic markers with more than one correlation value available in literature and yielding a homogeneous weighted r of 0.60 or above were considered to be superior. The meta-analysis identified four measures that met these criteria in sustained vowels and three measures in continuous speech. Although acoustic measures are routinely utilized in clinical voice examinations, the results of this meta-analysis suggest that caution is warranted regarding the concurrent validity and thus the clinical utility of many of these measures.

Acoustic measurement of overall voice quality: a meta-analysis.

Voice diseases have been increasing dramatically in recent times due mainly to unhealthy social habits and voice abuse. These diseases must be diagnosed and treated at an early stage, especially in the case of larynx cancer. It is widely recognized that vocal and voice diseases do not necessarily cause changes in voice quality as perceived by a listener. Acoustic analysis could be a useful tool to diagnose this type of disease. Preliminary research has shown that the detection of voice alterations can be carried out by means of Gaussian mixture models and short-term mel cepstral parameters complemented by frame energy together with first and second derivatives. This paper, using the F-Ratio and Fisher's discriminant ratio, will demonstrate that the detection of voice impairments can be performed using both mel cepstral vectors and their first derivative, ignoring the second derivative

Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters

A new method to calculate a spectral harmonics-to-noise ratio (HNR) in speech signals is presented. The method involves discrimination between harmonic and noise energy in the magnitude spectrum by...

A cepstrum-based technique for determining a harmonics-to-noise ratio in speech signals.

It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders-including glottic cancer-under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.

Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors

Parkinsons disease (PD) is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting about 1% of the people older than 65 and about 89% of the people with PD develop different speech disorders. Different researchers are currently working in the analysis of speech of people with PD, including the study of different dimensions in speech such as phonation, articulation,prosody and intelligibility. The study of phonation and articulation has been addressed mainly considering sustained vowels; however, the analysis of prosody and intelligibility requires the inclusion of words, sentences and monologue. In this paper we present a new database with speech recordings of 50 patients with PD and their respective healthy controls, matched by age and gender. All of the participants are Spanish native speakers and the recordings were collected following a protocol that considers both technical requirements and several recommendations given by experts in linguistics, phoniatry and neurology. This corpus includes tasks such as sustained phonations of the vowels, diadochokinetic evaluation, 45 words, 10 sentences, a reading text and a monologue. The paper also includes results of the characterization of the Spanish vowels considering different measures used in other works to characterize different speech impairments.

/pdf/new-spanish-speech-corpus-database-for-the-analysis-of-34l18qtvv5.pdf

New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease

The vocal quality of 64 normal subjects and 57 subjects suffering various degrees of glottal cancer was investigated using acoustic measures of six different aspects of the voice signal: tone perio...

Short-term stability measures for the evaluation of vocal quality.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

S. Feijoo

Author Tools

Chat about Author

Papers

Short-term stability measures for the evaluation of vocal quality.