A speech perturbation strategy based on “Lombard effect” for enhanced intelligibility for cochlear implant listeners

The goal of this study is to determine potential intelligibility benefits from Lombard speech for cochlear implant (CI) listeners in speech-in-noise conditions. “Lombard effect” (LE) is the natural response of adjusting speech production via auditory feedback due to noise exposure within acoustic environments. To evaluate intelligibility performance of natural and artificially induced Lombard speech, a corpus was generated to create natural LE from large crowd noise (LCN) exposure at 70, 80, and 90 dB sound pressure level (SPL). Clean speech was mixed with 15 and 10 dB SNR LCN and presented to five CI users. First, speech intelligibility was analyzed as a function of increasing LE and decreasing SNR. Results indicate significant improvements (p < 0.05) with Lombard speech intelligibility in noise conditions for 80 and 90 dB SPL. Next, an offline perturbation strategy was formulated to modify/perturb neutral speech so as to mimic LE through amplification of highly intelligible segments, uniform time stretching, and spectral mismatch filtering. This process effectively introduces aspects of LE into the neutral speech, with the hypothesis that this would benefit intelligibility for CI users. Significant (p < 0.01) intelligibility improvements of 13% and 16% percentage points were observed for 15 and 10 dB SNR conditions respectively for CI users. The results indicate how LE and LE-inspired acoustic and frequency-based modifications can be leveraged within signal processing to improve intelligibility of speech for CI users.The goal of this study is to determine potential intelligibility benefits from Lombard speech for cochlear implant (CI) listeners in speech-in-noise conditions. “Lombard effect” (LE) is the natural response of adjusting speech production via auditory feedback due to noise exposure within acoustic environments. To evaluate intelligibility performance of natural and artificially induced Lombard speech, a corpus was generated to create natural LE from large crowd noise (LCN) exposure at 70, 80, and 90 dB sound pressure level (SPL). Clean speech was mixed with 15 and 10 dB SNR LCN and presented to five CI users. First, speech intelligibility was analyzed as a function of increasing LE and decreasing SNR. Results indicate significant improvements (p < 0.05) with Lombard speech intelligibility in noise conditions for 80 and 90 dB SPL. Next, an offline perturbation strategy was formulated to modify/perturb neutral speech so as to mimic LE through amplification of highly intelligible segments, uniform time stretc...