Fractal characterization of speech waveform graphs

Pickover, Clifford A.; Khorasani, Al

doi:10.1016/0097-8493(86)90068-3

Cited by 74 publications

(23 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another group of features that are informative is the fractal features (Wu et al, 1992). Fractality analysis has been shown to be useful in audio analysis (Kumar and Johnson, 1993) and, in particular, analysis of waveform graphs such as speech audio (Pickover and Khorasani, 1986). As can be seen in the example Fig.…”

Section: Resultsmentioning

confidence: 85%

Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls

Shamir

Yerby

Simpson

et al. 2014

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds recorded by audio sensors carried by ten killer whales and eight pilot whales close to the coasts of Norway, Iceland, and the Bahamas were analyzed using computer methods and citizen scientists as part of the Whale FM project. Results show that the computer analysis automatically separated the killer whales into Icelandic and Norwegian whales, and the pilot whales were separated into Norwegian long-finned and Bahamas short-finned pilot whales, showing that at least some whales from these two locations have different acoustic repertoires that can be sensed by the computer analysis. The citizen science analysis was also able to separate the whales to locations by their sounds, but the separation was somewhat less accurate compared to the computer method.

show abstract

Section: Resultsmentioning

confidence: 85%

Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls

Shamir

Yerby

Simpson

et al. 2014

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

show abstract

“…The interest in fractals in speech date back to the mid-1980s (Pickover and Khorasani, 1986), and they have been used for a variety of applications, including consonant/vowel characterization (Martinez et al, 2003;Langi and Kinsner, 1995), speaker identification (Nelwamondo et al, 2006), and end-point detection (Li et al, 2007), even for whispered speech (Chen and Zhao, 2006). Recent research concerns the analysis of pathological voices through a fractal approach (Chouard et al, 2001;Ouayoun et al, 1999;Péan et al, 2000Péan et al, , 2002.…”

Section: Introductionmentioning

confidence: 99%

Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: A fractal dimension approach

López-de-Ipiña

Solé-Casals

Eguiraun

et al. 2015

Computer Speech & Language

View full text Add to dashboard Cite

Alzheimer's disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Western countries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by using automatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selected is based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work is feature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. The feature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters in the feature vector in order to enhance the performance of the original system while controlling the computational cost. © 2014 Elsevier Ltd. All rights reserved. E-mail addresses: karmele.ipina@ehu.es (K. López-de-Ipiña), jordi.sole@uvic.cat (J. Solé-Casals), harkaitz.eguiraun@ehu.es (H. Eguiraun), jesus.alonso@ulpgc.es (J.B. Alonso), carlos.travieso@ulpgc.es (C.M. Travieso), aitzol.ezeiza@ehu.es (A. Ezeiza), nora.barroso@ehu.es (N. Barroso), mecay@cita-alzheimer.org (M. Ecay-Torres), pmlage@cita-alzheimer.org (P. Martinez-Lage), mariablanca.beitia@ehu.es (B. Beitia).

show abstract

“…The interest on fractals in speech date back to the mid-80's [7], and they have been used for a variety of applications, including consonant/vowel characterization [8,9], speaker identification [10], and end-point detection [11], even for whispered speech [12]. Indeed, this metric has been also used in speech recognition, in some cases combined with MFCC's as described above [4].…”

Section: Introductionmentioning

confidence: 99%

Combining Mel Frequency Cepstral Coefficients and Fractal Dimensions for Automatic Speech Recognition

Ezeiza

López-de-Ipiña

Hernández

et al. 2011

Advances in Nonlinear Speech Processing

View full text Add to dashboard Cite

Abstract. Hidden Markov Models and Mel Frequency Cepstral Coefficients (MFCC's) are a sort of standard for Automatic Speech Recognition (ASR) systems, but they fail to capture the nonlinear dynamics of speech that are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful when training data is scarce, or when the ASR task is very complex. In this work, the Fractal Dimension (FD) of the observed time series is combined with the traditional MFCC's in the feature vector in order to enhance the performance of two different ASR systems: the first one is a very simple one, with very few training examples, and the second one is a Large Vocabulary Continuous Speech Recognition System for Broadcast News.

show abstract

Fractal characterization of speech waveform graphs

Cited by 74 publications

References 13 publications

Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls

Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls

Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: A fractal dimension approach

Combining Mel Frequency Cepstral Coefficients and Fractal Dimensions for Automatic Speech Recognition

Contact Info

Product

Resources

About