His primary scientific interests are in Speech recognition, Artificial intelligence, Feature extraction, Pattern recognition and Cepstrum. His Speech recognition research includes themes of Normalization and Noise. He interconnects Speech enhancement, Vocabulary, Filter and Finite impulse response in the investigation of issues within Artificial intelligence.
His Feature extraction research is multidisciplinary, incorporating perspectives in Noise reduction, Noise and Reverberation. His research integrates issues of Word error rate, White noise and Spectrogram in his study of Pattern recognition. Richard M. Stern combines subjects such as Signal-to-noise ratio, Robustness and Microphone with his study of Cepstrum.
Richard M. Stern mainly focuses on Speech recognition, Artificial intelligence, Pattern recognition, Speech processing and Binaural recording. His Speech recognition study deals with Feature extraction intersecting with Noise. His Artificial intelligence study combines topics in areas such as Noise and Natural language processing.
The concepts of his Pattern recognition study are interwoven with issues in Background noise and Robustness. His Speech processing study combines topics from a wide range of disciplines, such as Array processing, Beamforming, Reverberation and Speech coding. His studies in Binaural recording integrate themes in fields like Lateralization of brain function, Sound localization and Monaural.
Speech recognition, Artificial intelligence, Reverberation, Pattern recognition and Binaural recording are his primary areas of study. His work deals with themes such as Feature extraction, Mel-frequency cepstrum and Robustness, which intersect with Speech recognition. His research in Artificial intelligence intersects with topics in Smoothing, Algorithm and Spherical harmonics.
His Reverberation research integrates issues from Reduction, Noise measurement, Baseline system and Signal processing. His Pattern recognition study integrates concerns from other disciplines, such as Signal-to-noise ratio, Auditory masking and Noise. His Binaural recording research incorporates elements of Sound localization, Masking, Lateralization of brain function, Cognitive science and Monaural.
His primary scientific interests are in Speech recognition, Artificial intelligence, Pattern recognition, Feature extraction and Reverberation. His work on Speech processing as part of general Speech recognition study is frequently linked to Non-negative matrix factorization, therefore connecting diverse disciplines of science. His biological study deals with issues like Smoothing, which deal with fields such as Noise.
His research investigates the connection between Pattern recognition and topics such as Noise that intersect with problems in Feature extraction speech recognition, Speech coding, Computational model, Loudness compensation and Hidden Markov model. His study in the field of Speech recognition feature extraction also crosses realms of Event specific. The Reverberation study combines topics in areas such as Cepstrum, Mixture model, Robustness and Signal processing.
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A vector Taylor series approach for environment-independent speech recognition
P.J. Moreno;B. Raj;R.M. Stern.
international conference on acoustics speech and signal processing (1996)
An approach to cardiac arrhythmia analysis using hidden Markov models
D.A. Coast;R.M. Stern;G.G. Cano;S.A. Briller.
IEEE Transactions on Biomedical Engineering (1990)
Power-normalized cepstral coefficients (PNCC) for robust speech recognition
Chanwoo Kim;Richard M. Stern.
IEEE Transactions on Audio, Speech, and Language Processing (2016)
Environmental robustness in automatic speech recognition
A. Acero;R.M. Stern.
international conference on acoustics, speech, and signal processing (1990)
Missing-feature approaches in speech recognition
B. Raj;R.M. Stern.
IEEE Signal Processing Magazine (2005)
Reconstruction of missing features for robust speech recognition
Bhiksha Raj;Michael L. Seltzer;Richard M. Stern.
Speech Communication (2004)
Multiple approaches to robust speech recognition.
Richard M. Stern;Fu-Hua Liu;Yoshiaki Ohshima;Thomas M. Sullivan.
conference of the international speech communication association (1992)
Power-Normalized Cepstral Coefficients (PNCC) for robust speech recognition
Chanwoo Kim;Richard M. Stern.
international conference on acoustics, speech, and signal processing (2012)
Efficient cepstral normalization for robust speech recognition
Fu-Hua Liu;Richard M. Stern;Xuedong Huang;Alejandro Acero.
human language technology (1993)
Theory of binaural interaction based on auditory‐nerve data. IV. A model for subjective lateral position
Richard M. Stern;H. Steven Colburn.
Journal of the Acoustical Society of America (1978)
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