Singing Voice Quality: The Effects of Maxillary Dental Arch and Singing Style.

INTRODUCTION: In classical singing techniques, it is common to manipulate the vocal tract to channel airflow to increase voice quality and volume. Technique varies according to the style of the music, the voice type, and range of a given singer. Although these practices are intentional, fixed physiological aspects of a singer's vocal instrument also play an extremely impactful role in determining voice quality. OBJECTIVES: In the present study, the relationship between the dimensions of the maxillary dental arch and voice quality were examined in professional singers. METHODS: The dimensions of the palate were measured from the maxillary dental casts of 14 female singers. Audio recordings were made for the same participants while singing a sustained /a/ singing vowel, a glissando, the song "Are You Sleeping", and a selected song from their personal repertoire. The dimensions of the palate were measured from maxillary dental casts. From the recordings, two parameters were calculated: (1) the Singing Power Ratio (SPR) and (2) A2 A1 ratio. Higher SPR values indicate a stronger ring in the voice, typical of operatic singing style, while higher A2 A1 ratio values are associated with the belting singing style. RESULTS: Singers with larger frontal palate depth, smaller posterior palate depth, larger frontal palate width, and smaller posterior palate width seem to be more suitable for an operatic singing style. Singers who had larger overall depth and width of the palate measurements produced an increased second harmonic, typical of the belting style. CONCLUSIONS: When considering a singer's ability to produce vocalizations successfully, physiological structure is an increasingly important factor. The present study discovered that palate depth and width are associated with statistically significant differences in SPR and A2/A1 parameters. These parameters correlate with two styles of singing, operatic, and belting respectively.

Reproducibility of Voice Parameters: The Effect of Room Acoustics and Microphones.

INTRODUCTION: Computer analysis of voice recordings is an integral part of the evaluation and management of voice disorders. In many practices, voice samples are taken in rooms that are not sound attenuated and/or sound-proofed; further, the technology used is rarely consistent. This will likely affect the recordings, and therefore, their analyses. OBJECTIVES: The objective of this study is to compare various acoustic outcome measures taken from samples recorded in a sound-proofed booth to those recorded in more common clinic environments. Further, the effects from six different commonly used microphones will be compared. METHODS: Thirty-six speakers were recorded while reading a text and producing sustained vowels in a controlled acoustic environment. The collected samples were reproduced by a Head and Torso Simulator and recorded in three clinical rooms and in a sound booth using six different microphones. Newer measures (eg, Pitch Strength, cepstral peak prominence, Acoustic Voice Quality Index), as well as more traditional measures (eg Jitter, Shimmer, harmonics-to-noise ratio and Spectrum Tilt), were calculated from the samples collected with each microphone and within each room. RESULTS: The measures which are more robust to room acoustic differences, background noise, and microphone quality include Jitter and smooth cepstral peak prominence, followed by Shimmer, Acoustic Voice Quality Index, harmonics-to-noise ratio, Pitch Strength, and Spectrum Tilt. CONCLUSIONS: The effect of room acoustics and background noise on voice parameters appears to be stronger than the type of microphone used for the recording. Consequently, an appropriate acoustical clinical space may be more important than the quality of the microphone.

Influence of emotional expression, loudness, and gender on the acoustic parameters of vibrato in classical singers.

OBJECTIVES: Vibrato is considered one of the most essential characteristics of the classical singing voice. Vibrato can be acoustically described by the rate, extent, onset, and regularity. The aim of this study was to determine the influence of emotional expression on acoustic parameters of vibrato in classically trained singers. STUDY DESIGN: A prospective cohort study was performed. METHODS: Thirty healthy classical singers were recruited for this study, 29 singers met inclusion criteria. Inclusion criteria for this study were as follow: 1) no history of vocal pathology in the past year, 2) to have at least 5 years of classical singing training. Each subject was asked to sing the phrase "I Love You," while expressing four different emotions (tenderness, anger, happiness, and sadness) and without emotion (neutral state). The musical tonality of the phrase was adapted to each singer's vocal classification. Subjects were also recorded at three levels of loudness (pianissimo, mezzo forte, and fortissimo), while expressing each emotion. Acoustical analysis was performed during the vowel /o/ of the word "Love" to determine rate of vibrato, the extent of vibrato, and vibrato jitter. RESULTS: Vibrato parameters did not vary significantly when different emotions were expressed. However, vibrato jitter and extent did vary significantly between different levels of loudness. Significant differences were also noted in both rate and extent of vibrato when compared between sexes. CONCLUSIONS: Expression of emotions does not affect the acoustic parameters of vibrato, although some parameters are affected by loudness and gender.

A pattern recognition approach to spasmodic dysphonia and muscle tension dysphonia automatic classification.

Spasmodic dysphonia (SD) and muscle tension dysphonia (MTD) are two voice disorders that present similar characteristics. Usually, they can be differentiated only by experienced voice clinicians. There are many reasons that support the idea that SD is a neurological disease, requiring surgical treatments or, more usually, laryngeal botulinum toxin A injections as a therapeutic option. On the other hand, MTD is a functional disorder correctable with voice therapy. The importance of a correct diagnosis of these two disorders is critical at the treatment-selection moment. In this article, we present and compare the results of neural network and support vector machine-based methods that can help the clinicians to confirm their diagnosis. As a preliminary approach to the problem, we used only a sustained vowel /a/ to extract eight acoustic parameters. Then, a pattern recognition algorithm classifies the voice as normal, SD, or MTD. For comparison with previous works, we also separated the voices into normal and pathological (SD and MTD) voices with the methods proposed here. The results overcome the best classification rates between normal and pathological voices that have been previously reported, and demonstrate that our methods are very effective in distinguishing between MTD and SD.