Influence of Abusive Vocal Habits, Hydration, Mastication, and Sleep in the Occurrence of Vocal Symptoms in Teachers

Some vocal disorders in teachers are associated with occupational factors, but there are few studies that analyze the influence of vocal habits, fluid intake, mastication, and sleep on these disorders. The objective was to analyze the Occurrence of vocal fatigue, hoarseness, and dry throat in elementary and high school teachers and their association with vocal habits, fluid intake, mastication, and sleep. A sample of 422 elementary and secondary school teachers was Studied using a specific questionnaire. The multiple regression analysis showed that hoarseness was associated with absence of water intake (odds ratio (OR) = 1.7; P = 0.047), yelling/speaking loudly (OR = 1.6; P = 0.058), jaw-opening limitations (OR = 3.8; P = 0.003). average of: 6 hours of sleep/light (OR = 1.7; P = 0.039), and waking-up feeling replenished (OR = 2.0; P = 0.020). The presence of vocal fatigue was significantly associated with yelling/speaking loudly (OR = 2.2: P = 0.013), speaking excessively (OR = 2.4; P = 0.023), difficulty to open the mouth to masticate (OR = 6.6; P = 0.003), less than 6 hours of sleep (OR = 4.0; P = 0.008), and waking-up feeling replenished (sometimes OR = 2.8: P = 0.003; or never OR = 3.3 P = 0.002). The presence of dry throat was associated with being a former smoker (OR = 3.3; P = 0.011) and having jaw-opening limitations (OR = 3.9; P = 0.021). In recent years, speech and hearing interventions with teachers have focused on health-care promotion actions and prevention of vocal disorders, prioritizing issues related with hydration and healthy vocal use habits. However, the findings in the present study show the need to further focus on lifestyle habits related to sleep and eating habits.

Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.