Hypernasality Severity Detection Using Constant Q Cepstral Coefficients

Abstract

In this work, detection of hypernasality severity in cleft palate speech is attempted using constant Q cepstral coefficients (CQCC) feature. The coupling of nasal tract with the oral tract during the production of hypernasal speech adds nasal formants and anti-formants in low frequency region of vowel spectrum mainly around the first formant. The strength and position of nasal formants and anti-formants along with the oral formants changes as the severity of nasality changes in hypernasal speech. The CQCC feature is extracted from the constant Q transform (CQT) spectrum which employs geometrically spaced frequency bins and maintains a constant Q factor for across the entire spectrum. This results in a higher frequency resolution at lower frequencies and higher temporal resolution at higher frequencies. The CQT spectrum resolves the nasal and oral formants in low frequency and captures the spectral changes due to change in nasality severity. The CQCC feature gives the overall classification accuracy of 83.33 % and 78.47 % for /i/ and /u/ vowels corresponding to normal, mild and moderate-severe hypernasal speech, respectively using multiclass support vector classifier.

Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech

“…These improvements also demonstrate the effectiveness of our method. Besides, our method is capable of generality and can be applied to any neural networks used in previous works [10,12]. [8] 89.00 82.10 --Table 3: Results of hypernasality estimation on NMCPC and CNH cleft palate datasets.…”

“…[9] analyzed multiple different acoustic features for automatic identification of hypernasality. [10,11] also designed some novel acoustic features to better extract hypernasalityrelated semantics. These works mainly focused on extracting or designing advanced acoustic features for hypernasal speech detection.…”