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This paper evaluates the effectiveness of combining speech modification techniques that enhance transition components with active noise cancellation to improve the intelligibility of speech in noise. Two speech modification techniques were considered. One is based on wavelet-packet analysis, and the second uses a fixed filter, derived from time-frequency analysis, that emphasizes high frequencies. Active noise cancellation was provided by Bose noise-cancelling headphones. The test noise was real, generated by a ground auxiliary generator on the tarmac at an Air National Guard facility. The test signals were speech tokens from the modified rhyme (psycho-acoustic) test, recorded by a male speaker. This paradigm was used to measure word recognition rates at various signal-to-noise ratios. Active noise cancellation by itself provided over 40% increase in word recognition, while the modified speech and fixed filter techniques alone provided up to 20% improvement, depending on the signal-to-noise ratio. In combination, the speech modification approaches provided over 15% additional improvement in intelligibility over noise cancellation alone.

The role of transient speech components on speech intelligibility was investigated. Speech was decomposed into two components--quasi-steady-state (QSS) and transient--using a set of time-varying filters whose center frequencies and bandwidths were controlled to identify the strongest formant components in speech. The relative energy and intelligibility of the QSS and transient components were compared to original speech. Most of the speech energy was in the QSS component, but this component had low intelligibility. The transient component had much lower energy but was almost as intelligible as the original speech, suggesting that the transient component included speech elements important to speech perception. A modified version of speech was produced by amplifying the transient component and recombining it with the original speech. The intelligibility of the modified speech in background noise was compared to that of the original speech, using a psychoacoustic procedure based on the modified rhyme protocol. Word recognition rates for the modified speech were significantly higher at low signal-to-noise ratios (SNRs), with minimal effect on intelligibility at higher SNRs. These results suggest that amplification of transient information may improve the intelligibility of speech in noise and that this improvement is more effective in severe noise conditions.

The purpose of this study is to create information and signal processing algorithms to enhance the interpretation of speech in noise. An algorithm based on time-frequency analysis was employed to extract quasi-steady-state (QSS) energy from the speech signal, leaving a residual signal of predominantly transient components. The transient component was selectively amplified and recombined with the original speech to generate enhanced speech. The energy of the enhanced speech was adjusted to be equal to that of the original speech, and the intelligibility of the enhanced speech was compared to that of the original speech in background noise. Psychometric functions showed the enhanced speech had higher recognition scores at most noise levels, with greater improvement at lower SNRs. These results suggest that subjects can identify enhanced speech better than the original speech at severe noise conditions