# Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?
###### Abstract  
Automatic tools to detect hypernasality have been traditionally designed to analyze sustained vowels exclusively. This is in sharp contrast with clinical recommendations, which consider it necessary to use a variety of utterance types (e.g., repeated syllables, sustained sounds, sentences, etc.) This study explores the feasibility of detecting hypernasality automatically based on speech samples other than sustained vowels. The participants were 39 patients and 39 healthy controls. Six types of utterances were used: counting 1-to-10 and repetition of syllable sequences, sustained consonants, sustained vowel, words and sentences. The recordings were obtained, with the help of a mobile app, from Spain, Chile and Ecuador. Multiple acoustic features were computed from each utterance (e.g., MFCC, formant frequency) After a selection process, the best 20 features served to train different classification algorithms. Accuracy was the highest with syllable sequences and also with some words and sentences. Accuracy increased slightly by training the classifiers with between two and three utterances. However, the best results were obtained by combining the results of multiple classifiers. We conclude that protocols for automatic evaluation of hypernasality should include a variety of utterance types. It seems feasible to detect hypernasality automatically with mobile devices. hypernasality; Spanish language; speech acoustic features; ANN; automatic detection of speech deficits +
Footnote †: journal:  
specialized training in speech therapy (e.g., many otorhinolaryngologists, pediatricians or teachers). Part of the difficulty is due to nasality being a gradual phenomenon rather than a categorical one and to the fact that healthy speakers nasalize to some degree some oral sounds (as a result of poor coordination of the velum with other articulators, or to trans-palatal transmission of acoustic energy from the oral cavity to the nasal one [3]). In addition, in many languages such as Spanish, nasal vowels do not exist as phonemes, which means that most speakers have difficulties in recognizing nasal vowels as distinct categories. Finally, pathological HN often co-occurs with compensatory errors and/or low intelligibility, which may make it even harder to identify it [4]. These considerations have motivated researchers to develop objective measures of HN. One promising approach consists in using automatic classification systems trained with different sets of acoustic features [5]. This approach has two major advantages: it is non-invasive and the required technology is nowadays universally available (e.g., by using mobile phones). In the past there have been many proposals to evaluate HN based on acoustic information. While the results in terms of accuracy are generally excellent, most studies have used only a limited number of utterance types, such as sustained vowels [6; 7; 8; 9; 10; 11]. This is not clearly compatible with standard clinical recommendations [2], which strongly recommend that patients are evaluated using a variety of phonemes and utterances with varying complexity (e.g., CAPS-A protocol, [2; 12]). As regards phonemic diversity, the CAPS-A protocol identifies three levels of nasalization, depending on which segments are nasalized: (1) mild: nasalization evident only on closed vowels (e.g., \(/\,i\,u/\)); (2) moderate: nasalization observable in closed and open vowels (e.g., \(/\,a\,e\,o\,i\,u/\)); and (3) severe: nasalization observable in all vowels and in voiced consonants (e.g., \(/\,b\,\,d\,g/\)). Furthermore, [PERSON] et al. [12] proposes that syllable series including the voiceless stops such as \(/\,\)p \(\,\)t \(\,\)k\(/\) should be used to study nasalization. As regards to utterance complexity, it is generally emphasized that different utterance types are needed to provide valuable information (e.g., isolated vowels, words, sequences of syllables repetition, sentences and spontaneous speech [2; 12; 13; 14; 15; 16]). However, [PERSON] et al.