# Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?
###### Abstract
[12] consider that two of these tasks are especially helpful: on the one hand, repetition of syllabic sequences such as \(/\,ta\,ta\,ta\,ta\,\dots\,\) /, which allow to isolate individual phonemes and eliminate context effects; on the other, sentences that contain multiple productions of the same phoneme placement, which allow to assess the presence of nasal emission in a connected speech environment. To summarize, according to clinical experts, evaluation of HN should be based not only on isolated vowels but also on a variety of voiced and unvoiced consonants which are combined in different utterance types. Acoustic based studies have used two different approaches to detect HN. [PERSON] et al. [4] used Automatic Speech Recognition technology (ASR) to determine which sections of an utterance have been nasalized. To this end the authors trained an ASR system to classify audio segments as nasal-vowel, nasal-consonant, oral-vowel, or oral-consonant. While the results of this approach are most promising, it is important to note that it requires access to large, annotated corpora that are available only for a few languages and only for adult speech, which limits its applicability. A second approach consists in using classification algorithms trained with acoustic features of specific fragments of a speech signal. Classification algorithms include Random Forest (RF), Support Vector Machine (SVM) or Artificial Neural Network (ANN). Acoustic features used in previous studies include, among others, Mel Frequency Cepstrum Coefficients (MFCCs), the Voice Low Tone to High Tone Ratio (VLTHTR) and the vowel formants and their bandwidth [6; 7; 8; 9; 10; 11]. In most studies using this approach the fragments analyzed were either sustained vowels [6; 8] or vowel fragments that had been annotated manually in words or sentences [7; 9; 10; 11]. Only a few studies have used complex utterances to automatically evaluate nasality [17; 18; 19]. [PERSON] et al. [18] used six sentences containing stop and fricative consonants which are used routinely by speech therapists to perceptually assess the quality of speech. The authors trained an SVM classifier with a pool of acoustic features (e.g., jitter, shimmer, MFCC, bionic wavelet transform entropy and bionic wavelet transform energy) which were computed for each utterance. In the best case, accuracy reached 85% with a sensitivity of 82% and a specificity of 85%. [PERSON] et al. [17] analyzed a database of 108 healthy and 128 hypernasal Spanish speaking children. All the children produced the five Spanish sustained vowels and two words, one with unvoiced consonants (i.e., _koko_) and one with one voiced and one unvoiced consonant (i.e., _gato_). They trained an SVM classifier using non-linear dynamics features along with a set of six entropy measures. The results were the best for the vowels /a, i, e, o/ and for the word _gato_ (i.e., the one with a voiced consonant); the poorest results were observed with vowel /u/ and word _koko_. However, the results improved when they selected the best features from each vowel (accuracy 91%; sensibility: 93-95%; specificity: 88-90%). Altogether these results indicate that it is feasible to evaluate nasality automatically using running speech and also by combining different speech samples from the same speaker. To summarize, there seems to be a mismatch between clinical studies, on the one hand, which emphasize the importance of exploring a variety of speech sounds and utterance types and automatic analysis research, on the other, which has focused mainly on sustained vowels or a limited number of words or sentences. One obvious reason why most technical studies have used sustained vowels is because in such case the spectrum is stable throughout a relatively long window, which increases the probability of detecting the relatively small acoustic effects introduced by the nasal resonance.