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Hypernasality is an abnormal resonance in human speech production, especiallyin patients with craniofacial anomalies such as cleft palate. In clinicalapplication, hypernasality estimation is crucial in cleft palate diagnosis, asits results determine the subsequent surgery and additional speech therapy.Therefore, designing an automatic hypernasality assessment method willfacilitate speech-language pathologists to make precise diagnoses. Existingmethods for hypernasality estimation only conduct acoustic analysis based onlow-resource cleft palate dataset, by using statistical or neural network-basedfeatures. In this paper, we propose a novel approach that uses automatic speechrecognition model to improve hypernasality estimation. Specifically, we firstpre-train an encoder-decoder framework in an automatic speech recognition (ASR)objective by using speech-to-text dataset, and then fine-tune ASR encoder onthe cleft palate dataset for hypernasality estimation. Benefiting from suchdesign, our model for hypernasality estimation can enjoy the advantages of ASRmodel: 1) compared with low-resource cleft palate dataset, the ASR task usuallyincludes large-scale speech data in the general domain, which enables bettermodel generalization; 2) the text annotations in ASR dataset guide model toextract better acoustic features. Experimental results on two cleft palatedatasets demonstrate that our method achieves superior performance comparedwith previous approaches.

Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech