Autodirective microphone systems for natural communication with speech recognizers
HLT '91 Proceedings of the workshop on Speech and Natural Language
Speech recognition in noisy environments with the aid of microphone arrays
Speech Communication
HLT '90 Proceedings of the workshop on Speech and Natural Language
Towards environment-independent spoken language systems
HLT '90 Proceedings of the workshop on Speech and Natural Language
The acoustic-modeling problem in automatic speech recognition
The acoustic-modeling problem in automatic speech recognition
IEEE Concurrency
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In this paper, baseline speech recognition performance is determined both for a single remote microphone and for a signal derived from a delay-and-sum beamformer using an eight-microphone linear array. An HMM-based, connected-speech, 38-word vocabulary (alphabet, digits, 'space', 'period'), talker-independent speech recognition system is used for testing performance. Normal performance, with no language model, i.e., raw word-level performance, is currently about 81% for a set of talkers not in the training set and about 91% for training set data. The system has been trained and tested using a close-talking head-mounted microphone. Since a meaningful comparison requires using the same speech, the existing speech database was appropriately pre-filtered, played out through a transducer (speaker) in the room environment, picked-up by the microphone array, and re-stored as a digital file. The resulting file was post-processed and used as input to the recognizer; the recognition performance indicates the effect of the input device. The baseline experiment showed that both a single remote microphone and the beamformed signal reduced performance by 12% in a room with no other talkers. For the array tested, the error is generally attributable to reverberation off the floor and ceiling.