Recent Advances in Robust Speech Recognition Technology
Javier Ramírez, Juan Manuel Górriz
Bentham Science, Jan 1, 2011 - Computers - 210 pages
This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refers to the need to maintain high speech recognition accuracy even when the quality of the input speech is degraded, or when the acoustical, articulate, or phonetic characteristics of speech in the training and testing environments differ. Obstacles to robust recognition include acoustical degradations produced by additive noise, the effects of linear filtering, nonlinearities in transduction or transmission, as well as impulsive interfering sources, and diminished accuracy caused by changes in articulation produced by the presence of high-intensity noise sources. Although progress over the past decade has been impressive, there are significant obstacles to overcome before speech recognition systems can reach their full potential. Automatic speech recognition (ASR) systems must be robust to all levels, so that they can handle background or channel noise, the occurrence on unfamiliar words, new accents, new users, or unanticipated inputs. They must exhibit more 'intelligence' and integrate speech with other modalities, deriving the user's intent by combining speech with facial expressions, eye movements, gestures, and other input features, and communicating back to the user through multimedia responses. Therefore, as speech recognition technology is transferred from the laboratory to the marketplace, robustness in recognition becomes increasingly significant. This E-book should be useful to computer engineers interested in recent developments in speech recognition technology.
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accuracy acoustic adaptive algorithm approach Audio Processing AURORA automatic speech recognition average Bayesian Bayesian networks Cepstral clean speech CMVN coefficients computed covariance matrix database denotes distribution driving environments equation error estimation evaluation feature extraction frame frequency front-end GARCH model Gaussian hidden Markov model histogram equalization HOCMN ICASSP IEEE IEEE Trans improvement in-vehicle Kalman Kalman filter linear method MFCC microphone MMSE MO-LRT noise reduction noise suppression noisy signal noisy speech non-speech normalization observation obtained optimal outlier parameters PDF models performance PHEQ Proc proposed quantization reduce robust speech recognition segment Segura JC speaker speaker recognition spectral subtraction spectrum Speech and Audio speech and noise Speech Communication speech enhancement algorithms speech features speech processing speech quality speech recognition systems speech signal statistical model techniques testing sets tion training data utterance variance voice activity detection Wiener filter