Exploring vowel formant estimation through simulation-based techniques

2020 ◽  
Vol 6 (s1) ◽  
Author(s):  
Tyler Kendall ◽  
Charlotte Vaughn
Keyword(s):  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Fine Grained ◽  
Vowel Formant ◽  
Simulation Based ◽  
Insight Into

AbstractThis paper contributes insight into the sources of variability in vowel formant estimation, a major analytic activity in sociophonetics, by reviewing the outcomes of two simulations that manipulated the settings used for linear predictive coding (LPC)-based vowel formant estimation. Simulation 1 explores the range of frequency differences obtained when minor adjustments are made to LPC settings, and measurement timepoints around the settings used by trained analysts, in order to determine the range of variability that should be expected in sociophonetic vowel studies. Simulation 2 examines the variability that emerges when LPC settings are varied combinatorially around constant default settings, rather than settings set by trained analysts. The impacts of different LPC settings are discussed as a way of demonstrating the inherent properties of LPC-based formant estimation. This work suggests that differences more fine-grained than about 10 Hz in F1 and 15–20 Hz in F2 are within the range of LPC-based formant estimation variability.

scholarly journals Optimizing Vowel Formant Measurements in Four Acoustic Analysis Systems for Diverse Speaker Groups

2016 ◽  
Vol 25 (3) ◽  
pp. 335-354 ◽  
Author(s):  
Ekaterini Derdemezis ◽  
Houri K. Vorperian ◽  
Ray D. Kent ◽  
Marios Fourakis ◽  
Emily L. Reinicke ◽  
...  
Keyword(s):  
Acoustic Analysis ◽  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Formant Frequency ◽  
Male And Female ◽  
Consensus Analysis ◽  
Optimal Analysis ◽  
Software Packages ◽  
Vowel Formant ◽  
Female Children

Purpose This study systematically assessed the effects of select linear predictive coding (LPC) analysis parameter manipulations on vowel formant measurements for diverse speaker groups using 4 trademarked Speech Acoustic Analysis Software Packages (SAASPs): CSL, Praat, TF32, and WaveSurfer. Method Productions of 4 words containing the corner vowels were recorded from 4 speaker groups with typical development (male and female adults and male and female children) and 4 speaker groups with Down syndrome (male and female adults and male and female children). Formant frequencies were determined from manual measurements using a consensus analysis procedure to establish formant reference values, and from the 4 SAASPs (using both the default analysis parameters and with adjustments or manipulations to select parameters). Smaller differences between values obtained from the SAASPs and the consensus analysis implied more optimal analysis parameter settings. Results Manipulations of default analysis parameters in CSL, Praat, and TF32 yielded more accurate formant measurements, though the benefit was not uniform across speaker groups and formants. In WaveSurfer, manipulations did not improve formant measurements. Conclusions The effects of analysis parameter manipulations on accuracy of formant-frequency measurements varied by SAASP, speaker group, and formant. The information from this study helps to guide clinical and research applications of SAASPs.

Mental time travel? A neurocognitive model of event simulation

2020 ◽  
Author(s):  
Donna Rose Addis
Keyword(s):  
Predictive Coding ◽  
Time Travel ◽  
Simulation System ◽  
Mental Time Travel ◽  
Event Representation ◽  
Conceptual Information ◽  
Fine Grained ◽  
The Past ◽  
The Future ◽  
General Capacity

Mental time travel (MTT) is defined as projecting the self into the past and the future. Despite growing evidence of the similarities of remembering past and imagining future events, dominant theories conceive of these as distinct capacities. I propose that memory and imagination are fundamentally the same process – constructive episodic simulation – and demonstrate that the ‘simulation system’ meets the three criteria of a neurocognitive system. Irrespective of whether one is remembering or imagining, the simulation system: (1) acts on the same information, drawing on elements of experience ranging from fine-grained perceptual details to coarser-grained conceptual information and schemas about the world; (2) is governed by the same rules of operation, including associative processes that facilitate construction of a schematic scaffold, the event representation itself, and the dynamic interplay between the two (cf. predictive coding); and (3) is subserved by the same brain system. I also propose that by forming associations between schemas, the simulation system constructs multi-dimensional cognitive spaces, within which any given simulation is mapped by the hippocampus. Finally, I suggest that simulation is a general capacity that underpins other domains of cognition, such as the perception of ongoing experience. This proposal has some important implications for the construct of ‘MTT’, suggesting that ‘time’ and ‘travel’ may not be defining, or even essential, features. Rather, it is the ‘mental’ rendering of experience that is the most fundamental function of this simulation system, enabling humans to re-experience the past, pre-experience the future, and also comprehend the complexities of the present.

scholarly journals Navigation Security Module with Real-Time Voice Command Recognition System

2017 ◽  
Vol 24 (2) ◽  
pp. 17-26
Author(s):  
Mustafa Yagimli ◽  
Huseyin Kursat Tezer
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Predictive Coding ◽  
Recognition System ◽  
Time Warping ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Voice Command ◽  
Security Module ◽  
Dynamic Time

Abstract The real-time voice command recognition system used for this study, aims to increase the situational awareness, therefore the safety of navigation, related especially to the close manoeuvres of warships, and the courses of commercial vessels in narrow waters. The developed system, the safety of navigation that has become especially important in precision manoeuvres, has become controllable with voice command recognition-based software. The system was observed to work with 90.6% accuracy using Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) parameters and with 85.5% accuracy using Linear Predictive Coding (LPC) and DTW parameters.

scholarly journals Optimalisasi Metode LPC-16 dan HMM-Forward Pada Sistem Asisten Virtual

2021 ◽  
Vol 7 (2) ◽  
pp. 101-107
Author(s):  
Chondro seto Nur Suryawan ◽  
Marisa Premitasari
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Predictive Coding ◽  
Linear Predictive Coding

Pada umumnya manusia saat ini menggunakan sistem operasi windows yang berjalan di perangkat desktop akan memasang banyak aplikasi sesuai kebutuhannya. Semakin banyak aplikasi yang di pasang maka semakin banyak pula shortcut yang tampil di bagian desktop windows. Shortcut sendiri merupakan sebuah objek alternatif yang digunakan untuk mewakili sehingga pengguna dapat dengan mudah membuka aplikasi tanpa harus pengguna membuka tempat dimana aplikasi tersebut terpasang. Banyaknya aplikasi yang terpasang pada sistem operasi windows membuat shortcut pada bagian desktop menjadi banyak dan membuat pengguna kesulitan dalam mencari  atau membuka aplikasi yang dinginkan. Oleh karena itu diperlukan aplikasi yang dapat membantu pengguna dalam mencari dan membuka aplikasi dengan mudah tanpa membuat pengguna kesulitan. Aplikasi tersebut adalah virtual asisten yang akan membantu pengguna dalam mencari dan membuka aplikasi yang diinginkan. Cara kerjanya dengan pengguna memasukan suara pengguna lalu di proses ekstraksi ciri menggunakan metode Linear Predictive Coding lalu di klasifikasikan menggunakan metode Hidden Markov Model Forward. Setelah terdeteksi maka aplikasi akan membuka aplikasi sesuai suara yang terdeksi. Penelitian ini menggunakan 120 data latih yang terdiri dari 6 label yaitu whatsapp, linkedin, Tokopedia, gmail, powerpoint, word. Untuk setiap label memiliki data latih berjumlah 20 data. Data yang diujikan berjumlah 60. Untuk setiap labelnya memiliki 10 data uji.

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