A Simple Speech Production System Based on Formant Estimation of a Tongue Articulatory System Using Human Tongue Orientation

Padmini, Palli; Gupta, Deepa; Zakariah, Mohammed; Alotaibi, Yousef Ajami; Bhowmick, Kaustav

doi:10.1109/access.2020.3048076

Cited by 5 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Frequency related features: Pitch (F0): Vocal cords vibration frequency (the fundamental frequency) that exists only in voiced speech (e.g., vowels). Voiced Vocalization (VV) was defined as a vocalization where most of its frames (≥ 60%) 10 were voiced (voicing threshold 0.45). Formants: The resonant frequencies of the vocal tract that shape vowel sounds 36 . The first two formants (F1 and F2) relate to tongue position (vertical and horizontal) and influence vowel quality.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech

Eni,

Ilan,

Michaelovski

et al. 2023

Preprint

View full text Add to dashboard Cite

Social communication problems, which are a core symptom of autism, may be evident in the speech characteristics of autistic individuals. Here, we examined acoustic and conversational features extracted from audio recordings of Autism Diagnostic Observation Schedule, 2ndedition (ADOS-2) assessments of 1.5-7-year-old children. We trained a deep neural network algorithm to estimate autism severity (i.e., ADOS-2 scores) from recordings of 146 children and tested its accuracy with independent recordings from 62 additional children who completed two ADOS-2 assessments, separated by 1-2 years. Estimated ADOS-2 social affect scores in the test set were significantly correlated with true scores at each time-point (r(62)=0.442-0.575,P<0.001), and estimated changes across time-points were significantly correlated with true changes (r(62)=0.343,P=0.011). The presented algorithm learned to estimate social symptom severity from speech recordings of one autism group and was able to accurately estimate severity changes in an entirely independent group. While accuracy needs to be further improved by training with larger datasets, these results demonstrate the remarkable utility of speech analysis algorithms in estimating autism risk and severity changes over time.

show abstract

Section: Methodsmentioning

confidence: 99%

“… Formants: The resonant frequencies of the vocal tract that shape vowel sounds [40]. The first two formants (F1 and F2) relate to tongue position (vertical and horizontal) and influence vowel quality.…”

Section: Frequency Related Featuresmentioning

confidence: 99%

Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech

Eni,

Ilan,

Michaelovski

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Off late, algorithms have been developed to facilitate straightforward communication through synthesized speech for those with total or partial loss of speech. They include electrolarynx [6], sign to speech converter [7], text to speech synthesis [8], silent sound technology [9], vocal cord vibration switches [10], articulatory speech synthesizers [11], brain implants [12], breath to speech [13], and, tongue articulatory systems [14]. They are based on inputs captured through hand gestures, text data, lip movements, vocalizations, visual features, brain signals, exhales, and tongue movements respectively.…”

Section: Introductionmentioning

confidence: 99%

“…They are based on inputs captured through hand gestures, text data, lip movements, vocalizations, visual features, brain signals, exhales, and tongue movements respectively. Such models can be useful to interpret speech for tracheostomized patients who have undergone larynx surgery, those who are speechdisabled due to accidents or voice disorders, medical rehabilitation, and robotics [6][7][8][9][10][11][12][13][14]. However, such techniques are used to synthesize the speech, whose voice is chosen either of google voice or robotic voice or a universal or generated voice database, where the speaker does not sound natural.…”

Section: Introductionmentioning

confidence: 99%

Age-Based Automatic Voice Conversion Using Blood Relation for Voice Impaired

Padmini¹,

Paramasivam²,

Lal³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

The present work presents a statistical method to translate human voices across age groups, based on commonalities in voices of blood relations. The age-translated voices have been naturalized extracting the blood relation features e.g., pitch, duration, energy, using Mel Frequency Cepstrum Coefficients (MFCC), for social compatibility of the voice-impaired. The system has been demonstrated using standard English and an Indian language. The voice samples for resynthesis were derived from 12 families, with member ages ranging from 8-80 years. The voice-age translation, performed using the Pitch synchronous overlap and add (PSOLA) approach, by modulation of extracted voice features, was validated by perception test. The translated and resynthesized voices were correlated using Linde, Buzo, Gray (LBG), and Kekre's Fast Codebook generation (KFCG) algorithms. For translated voice targets, a strong (θ >∼93% and θ >∼96%) correlation was found with blood relatives, whereas, a weak (θ <∼78% and θ <∼80%) correlation range was found between different families and different gender from same families. The study further subcategorized the sampling and synthesis of the voices into similar or dissimilar gender groups, using a support vector machine (SVM) choosing between available voice samples. Finally, ∼96%, ∼93%, and ∼94% accuracies were obtained in the identification of the gender of the voice sample, the age group samples, and the correlation between the original and converted voice samples, respectively. The results obtained were close to the natural voice sample features and are envisaged to facilitate a near-natural voice for speech-impaired easily.

show abstract

A Quest for Formant-Based Compact Nonuniform Trapezoidal Filter Banks for Speech Processing with VGG16

Parlak,

Altun

2024

Circuits Syst Signal Process

View full text Add to dashboard Cite

A Simple Speech Production System Based on Formant Estimation of a Tongue Articulatory System Using Human Tongue Orientation

Cited by 5 publications

References 36 publications

Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech

Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech

Age-Based Automatic Voice Conversion Using Blood Relation for Voice Impaired

A Quest for Formant-Based Compact Nonuniform Trapezoidal Filter Banks for Speech Processing with VGG16

Contact Info

Product

Resources

About