title: Toward optimizing stream fusion in multistream recognition of speech.
abstract: A multistream phoneme recognition framework is proposed based on forming streams from different spectrotemporal modulations of speech. Phoneme posterior probabilities were estimated from each stream separately and combined at the output level. A statistical model of the final estimated posterior probabilities is used to characterize the system performance. During the operation, the best fusion architecture is chosen automatically to maximize the similarity of output statistics to clean condition. Results on phoneme recognition from noisy speech indicate the effectiveness of the proposed method.
authors.affiliation: Department of Neurological Surgery, University of California, San Francisco, California 94122, USA. nmesgar1@jhu.edu
authors.forename: Nima
authors.initials: N
authors.lastname: Mesgarani
authors.forename: Samuel
authors.initials: S
authors.lastname: Thomas
authors.forename: Hynek
authors.initials: H
authors.lastname: Hermansky
mesh_terms: D005583:Fourier Analysis; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009622:Noise / Q000009:adverse effects*; D010363:Pattern Recognition, Automated; D010700:Phonetics; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D049250:Speech Recognition Software; D013997:Time Factors