A pitch-adaptive MFCC feature is used for hypernasality detection. The feature is derived from the cepstral smooth spectrum instead of magnitude spectrum.
Sep 2, 2018 · The important works on hypernasality detection are based on Teager energy operator (TEO) based feature [11], TEO feature with frequency cepstral ...
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Hypernasality detection using zero time windowing. DubeyA.K. et al. Pitch-adaptive front-end feature for hypernasality detection. Proc. Interspeech. (2018).
A feature-independent end-to-end algorithm that uses a convolutional neural network (CNN) to detect hypernasality in cleft palate speech is described and it ...
Pitch-Adaptive Front-end Feature for Hypernasality Detection. AK Dubey, SRM Prasanna, S Dandapat. Interspeech, 372-376, 2018. 14, 2018 ; Zero time windowing ...
Download scientific diagram | Block diagram for the extraction of the pitch-adaptive MFCC feature by applying adaptive-liftering for spectral smoothening.
The features are based on two acoustic models trained on a large corpus of healthy speech. The first acoustic model aims to measure nasal resonance from voiced ...
A group delay-based signal processing technique for the analysis and detection of hypernasal speech, using a band-limited approach to estimate the locations ...
Oct 7, 2024 · Variational mode decomposition based features for detection of hypernasality ... Pitch-Adaptive Front-end Feature for Hypernasality Detection.
Aug 1, 2019 · Automatic hypernasality detection in cleft palate speech can facilitate diagnosis by speech-language pathologists.