The Journal of the Acoustical Society of America · 2005 · 283 citations · 61 references
EngineeringSpeech Reception ThresholdSpeech IntelligibilityStationary NoiseSpeech EnhancementCommunicationSpeech RecognitionSpeech CodingNoiseRobust Speech RecognitionInformational MaskingHealth SciencesComputer EngineeringSignal ProcessingSpeech CommunicationSpeech TechnologyHearing LossSpeech AnalysisSpeech ProcessingSii ModelNormal-hearing ListenersSpeech PerceptionLinguistics
The SII model accurately predicts intelligibility in stationary noise but fails for nonstationary maskers, where informational masking may contribute. The study proposes an extension to the SII model to predict speech intelligibility in both stationary and fluctuating noise. The approach partitions speech and noise into short time frames, computes conventional SII per frame, and averages these values to obtain the overall SII for the condition. Using literature SRT data, the extended SII model accurately predicts SRTs for stationary, fluctuating speech, interrupted, and multi-talker noise, and improves predictions for SIM and speech‑like maskers, though still imperfect.
The SII model in its present form (ANSI S3.5-1997, American National Standards Institute, New York) can accurately describe intelligibility for speech in stationary noise but fails to do so for nonstationary noise maskers. Here, an extension to the SII model is proposed with the aim to predict the speech intelligibility in both stationary and fluctuating noise. The basic principle of the present approach is that both speech and noise signal are partitioned into small time frames. Within each time frame the conventional SII is determined, yielding the speech information available to the listener at that time frame. Next, the SII values of these time frames are averaged, resulting in the SII for that particular condition. Using speech reception threshold (SRT) data from the literature, the extension to the present SII model can give a good account for SRTs in stationary noise, fluctuating speech noise, interrupted noise, and multiple-talker noise. The predictions for sinusoidally intensity modulated (SIM) noise and real speech or speech-like maskers are better than with the original SII model, but are still not accurate. For the latter type of maskers, informational masking may play a role.
61