title: Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemes.
abstract: The aim of this study is to quantify the gap between the recognition performance of human listeners and an automatic speech recognition (ASR) system with special focus on intrinsic variations of speech, such as speaking rate and effort, altered pitch, and the presence of dialect and accent. Second, it is investigated if the most common ASR features contain all information required to recognize speech in noisy environments by using resynthesized ASR features in listening experiments. For the phoneme recognition task, the ASR system achieved the human performance level only when the signal-to-noise ratio (SNR) was increased by 15 dB, which is an estimate for the human-machine gap in terms of the SNR. The major part of this gap is attributed to the feature extraction stage, since human listeners achieve comparable recognition scores when the SNR difference between unaltered and resynthesized utterances is 10 dB. Intrinsic variabilities result in strong increases of error rates, both in human speech recognition (HSR) and ASR (with a relative increase of up to 120%). An analysis of phoneme duration and recognition rates indicates that human listeners are better able to identify temporal cues than the machine at low SNRs, which suggests incorporating information about the temporal dynamics of speech into ASR systems.
authors.affiliation: Medizinische Physik, Carl-von-Ossietzky Universität Oldenburg, D-26111 Oldenburg, Germany. bernd.meyer@uni-oldenburg.de
authors.forename: Bernd T
authors.initials: BT
authors.lastname: Meyer
authors.forename: Thomas
authors.initials: T
authors.lastname: Brand
authors.forename: Birger
authors.initials: B
authors.lastname: Kollmeier
mesh_terms: D000161:Acoustic Stimulation; D001301:Audiometry, Pure-Tone; D001302:Audiometry, Speech; D001309:Auditory Threshold; D003463:Cues; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D009622:Noise / Q000009:adverse effects; D010363:Pattern Recognition, Automated; D046709:Pattern Recognition, Physiological; D010470:Perceptual Masking; D010700:Phonetics; D021641:Recognition, Psychology; D013061:Speech Acoustics; D013067:Speech Perception; D049250:Speech Recognition Software; D013997:Time Factors; D013998:Time Perception