What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Speech recognition
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.
His most cited work include:
- Environmental robustness in automatic speech recognition (428 citations)
- An approach to cardiac arrhythmia analysis using hidden Markov models (422 citations)
- A vector Taylor series approach for environment-independent speech recognition (418 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Speech recognition (73.33%)
- Artificial intelligence (38.82%)
- Pattern recognition (26.67%)
What were the highlights of his more recent work (between 2010-2021)?
- Speech recognition (73.33%)
- Artificial intelligence (38.82%)
- Reverberation (9.80%)
In recent papers he was focusing on the following fields of study:
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.
Between 2010 and 2021, his most popular works were:
- Power-Normalized Cepstral Coefficients (PNCC) for robust speech recognition (220 citations)
- Power-normalized cepstral coefficients (PNCC) for robust speech recognition (128 citations)
- Delta-spectral cepstral coefficients for robust speech recognition (65 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Speech recognition
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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