Introduction Individuals with idiopathic subglottic stenosis (SGS) are at risk for voice disorders prior to and following surgical management. This study examined the nature and severity of voice disorders in patients with SGS before and after a revised cricotracheal resection procedure designed to minimize adverse effects on voice function. Method Eleven women with idiopathic SGS provided presurgical and postsurgical audio recordings. Voice Handicap Index (VHI) scores were also collected. Cepstral, signal-to-noise, periodicity, and fundamental frequency analyses were undertaken for connected speech and sustained vowel samples. Listeners made auditory-perceptual ratings of overall quality and monotonicity. Results Paired samples statistical analyses revealed that mean fundamental frequency decreased from 215 Hz (SD=40 Hz) to 201 Hz (SD=65 Hz) following surgery. In general, VHI scores decreased after surgery. Voice disorder severity based on the cepstral spectral index of dysphonia™ for sustained vowels decreased (improved) from 41 (SD=41) to 25 (SD=21) points; no change was observed for connected speech. Semitone standard deviation (2.2 semitones) did not change from pre- to post-treatment. Auditory-perceptual ratings demonstrated similar results. Conclusions These preliminary results indicate that this revised cricotracheal resection procedure is promising in minimizing adverse voice effects while offering a longer-term surgical outcome for SGS. Further research is needed to determine causal factors for pretreatment voice disorders, as well as to optimize treatments in this population.

School teachers have an elevated risk of voice problems due to the vocal demands in the workplace. This manuscript presents the results of three studies investigating teachers’ voice use at work. In the first study, 57 teachers were observed for 2 weeks (waking hours) to compare how they used their voice in the school environment and in non-school environments. In a second study, 45 participants performed a short vocal task in two different rooms: a variable acoustic room and an anechoic chamber. Subjects were taken back and forth between the two rooms. Each time they entered the variable acoustics room, the reverberation time and/or the background noise condition had been modified. In this latter study, subjects responded to questions about their vocal comfort and their perception of changes in the acoustic environment. In a third study, 20 untrained vocalists performed a simple vocal task in the following conditions: with and without background babble and with and without transparent plexiglass shields to increase the first reflection. Relationships were examined between [1] the results for the room acoustic parameters; [2] the subjects’ perception of the room; and [3] the recorded speech acoustic. Several differences between male and female subjects were found; some of those differences held for each room condition (at school vs. not at school, reverberation level, noise level, and early reflection).

In both practicing audiology and speech language pathology, as well as in speech and hearing science research, the space where the work is done is an integral part of the function. Hence, for all of these endeavors it can be important to measure the acoustics of a room. This article provides a tutorial regarding the measurement of room reverberation and background noise, both of which are important when evaluating a space’s strengths and limitations for speech communication. As the privacy of patients and research participants is a primary concern, the tutorial also describes a method for measuring the amount of acoustical insulation provided by a room’s barriers. Several room measurement data sets – all obtained from the assessment of clinical and research spaces within our own department – are presented here as examples.

As a person ages, the acoustic characteristics of the voice change. Understanding how the sound of a voice changes with age may give insight into physiological changes related to vocal function. Previous work has shown changes in acoustical parameters with chronological age, as well as differences between listener-perceived age and chronological age. However, much of this previous work was done using cross-sectional speech samples, which will show changes with age but may average out important variability with regard to individual aging differences. The current study used a longitudinal recording sample gathered from a corpus of speeches from a single individual spanning about 50 years (48 to 97 years of age). This study investigates how the voice changes with age using both chronological age and perceived age as independent variables; perceived age data were obtained in a previous direct age estimation study. Using the longitudinal recordings, a range of voice and speech acoustic parameters were extracted. These parameters were fitted to a supervised learning model to predict chronological age and perceived age. Differences between the chronological age and perceived age models as well as the usefulness of the various acoustic parameters will be discussed.

As a person ages, the acoustic characteristics of their voice change. Understanding how the sound of a voice changes with age may give insight into physiological changes related to vocal function. Previous work has shown changes in acoustical parameters with chronological age as well as differences between perceived age and chronological age. However, much of this previous work was done using cross-sectional speech samples, which will show changes with age but may average out important individual variability with regard to aging differences. The current study used a longitudinal recording sample gathered from a corpus of speeches from an individual spanning about 50 years (48 to 97 years of age). This study investigates how the voice changes with age using both chronological age and perceived age as independent variables; perceived age data were obtained in a previous direct age estimation study. Using the longitudinal recordings, a range of voice and speech acoustic parameters were extracted. These acoustic parameters were fitted to a supervised learning model to predict chronological age and perceived age. Differences between the chronological age and perceived age models as well as the usefulness of the various acoustic parameters will be discussed.