Vocal effort is a physiological measure that accounts for changes in voice production as vocal loading increases. It has been quantified in terms of sound pressure level (SPL). This study investigates how vocal effort is affected by speaking style, room acoustics, and short-term vocal fatigue. Twenty subjects were recorded while reading a text at normal and loud volumes in anechoic, semi-reverberant, and reverberant rooms in the presence of classroom babble noise. The acoustics in each environment were modified by creating a strong first reflection in the talker position. After each task, the subjects answered questions addressing their perception of the vocal effort, comfort, control, and clarity of their own voice. Variation in SPL for each subject was measured per task. It was found that SPL and self-reported effort increased in the loud style and decreased when the reflective panels were present and when reverberation time increased. Self-reported comfort and control decreased in the loud style, while self-reported clarity increased when panels were present. The lowest magnitude of vocal fatigue was experienced in the semi-reverberant room. The results indicate that early reflections may be used to reduce vocal effort without modifying reverberation time.

Summary Speakers increase their vocal effort when their communication is disturbed by noise. This adaptation is termed the Lombard effect. The aim of the present study was to determine whether this effect has a starting point. Hence, the effects of noise at levels between 20 and 65 dB(A) on vocal effort (quantified by sound pressure level) and on both perceived noise disturbance and perceived vocal discomfort were evaluated. Results indicate that there is a Lombard effect change-point at a background noise level (Ln) of 43.3 dB(A). This change-point is anticipated by noise disturbance, and is followed by a high magnitude of vocal discomfort.

In recent years, rapid advances in speech technology have been made possible by machine learning challenges such as CHiME, REVERB, Blizzard, and Hurricane. In the Clarity project, the machine learning approach is applied to the problem of hearing aid processing of speech-in-noise, where current technology in enhancing the speech signal for the hearing aid wearer is often ineffective. The scenario is a (simulated) cuboid-shaped living room in which there is a single listener, a single target speaker and a single interferer, which is either a competing talker or domestic noise. All sources are static, the target is always within ±30 • azimuth of the listener and at the same elevation, and the interferer is an omnidirectional point source at the same elevation. The target speech comes from an open source 40speaker British English speech database collected for this purpose. This paper provides a baseline description of the round one Clarity challenges for both enhancement (CEC1) and prediction (CPC1). To the authors' knowledge, these are the first machine learning challenges to consider the problem of hearing aid speech signal processing.

Speakers adjust their vocal effort when communicating in different room acoustic and noise conditions and when instructed to speak at different volumes. The present paper reports on the effects of voice style, noise level, and acoustic feedback on vocal effort, evaluated as sound pressure level, and self-reported vocal fatigue, comfort, and control. Speakers increased their level in the presence of babble and when instructed to talk in a loud style, and lowered it when acoustic feedback was increased and when talking in a soft style. Self-reported responses indicated a preference for the normal style without babble noise.

No abstract