Prediction and Optimization of Speech Intelligibility in Adverse Conditions
Cees Taal · Research Repository (Delft University of Technology) · 2013
In digital speech-communication systems like mobile phones, public address systems and hearing aids, conveying the message is one of the most important goals. This can be challenging since the intelligibility of the speech may be harmed at various stages before, during and after the transmission process from sender to receiver. Causes which create such adverse conditions include background noise, an unreliable internet connection during a Skype conversation or a hearing impairment of the receiver. To overcome this, many speech-communication systems include speech processing algorithms to compensate for these signal degradations like noise reduction. To determine the effect on speech intelligibility of these signal processing based solutions, the speech signal has to be evaluated by means of a listening test with human listeners. However, such tests are costly and time consuming. As an alternative, reliable and fast machine-driven intelligibility predictors are of interest, since they might replace listening tests, at least in some stages of the algorithm development process. Two important issues exist with current intelligibility predictors. (1) Many of these methods cannot reliably predict the effect of more advanced nonlinear signal processing algorithms on speech intelligibility. (2) Typically, these measures are based on very complex auditory models or use average statistics of minutes of running speech, which makes it difficult on how to design new (real-time) speech processing solutions in an optimal manner given such a measure. To this end we propose several new measures which show good prediction results with the intelligibility of nonlinear processed speech. The newly proposed measures are of a low computational complexity and mathematically tractable which make them suitable for optimization of new signal processing solutions which aim for improving speech intelligibility.