Optimizing Speech Intelligibility in a Noisy Environment: A unified view

Abstract

Modern communication technology facilitates communication from-anywhere to-anywhere. As a result, low speech intelligibility has become a common problem, which is exacerbated by the lack of feedback to the talker about the rendering environment. In recent years, a range of algorithms has been developed to enhance the intelligibility of speech rendered in a noisy environment. We describe methods for intelligibility enhancement from a unified vantage point. Before one defines a measure of intelligibility, the level of abstraction of the representation must be selected. For example, intelligibility can be measured on the message, on the sequence of words spoken, on the sequence of sounds, or on a sequence of states of the auditory system. Natural measures of intelligibility defined at the message level are mutual information and the hit-or-miss criterion. The direct evaluation of high-level measures requires quantitative knowledge of human cognitive processing. Lower level measures can be derived from higher level measures by making restrictive assumptions. We discuss the implementation and performance of some specific enhancement systems in detail, including speech intelligibility index (SII) based systems and systems aimed at enhancing the sound-field where it is perceived by the listener. We conclude with a discussion of the current state of the field and open problems.

An intelligibility metric based on a simple model of speech communication

“…Recently, information theory (IT) has been proposed as a new paradigm for speech intelligibility prediction [13,14,15]. This is a natural approach to take given that the fundamental goal of speech communication is to transfer information from a talker to a listener.…”

Improving speech privacy in personal sound zones

“…High mutual information between the clean speech (talker), y(n), and the degraded speech (listener), p(x, n) from (4), is attained at high signal to noise ratio (SNR) [17], hence indicating that reducing the SNR, for example by adding noise, reduces intelligibility. In this work the intelligibility for two signals x1(n) and x2(n) is denoted as I(x1; x2).…”

Does the Lombard Effect Improve Emotional Communication in Noise? — Analysis of Emotional Speech Acted in Noise

“…It is also applicable to the study of vocal disorders and speech production, and has even been used as a therapeutic tool to improve speech intelligibility in Parkinson's disease patients [7]. In architectural acoustics and design, studies on the Lombard effect have been utilized to reduce unwanted noise and improve intelligibility of speech indoors [19]. The Lombard effect is relevant to the study of phonetics and linguistics [7], too.…”