The implementation and evaluation of an automatic speech recognition (ASR) based task for a mobile, hand\u2010held device is presented. The use of ASR on mobile devices will make environmental noise, and the design of transducers and compensation algorithms for dealing with noise, critical issues for the success of ASR based services on these devices. A description of the task is provided along with a set of compensation techniques that are used to compensate speaker independent hidden Markov models (HMMs) with respect to environment and transducer variability. Data were collected in a prototype application environment for a form\u2010filling directory access task with speech and pen input and text output (48 subjects in an office environment; 21 in a cafeteria). A technique for combined environment\/transducer compensation is presented for this task and is shown to significantly reduce the effects of environmental mismatch. The overall performance degradation was reduced from 41.7% to 10.4% for speech spoken through a far\u2010field microphone in an office environment, and from 79.2% to 39.8% for the same transducer in a noisy cafeteria environment.<\/p>\n","protected":false},"excerpt":{"rendered":"
The implementation and evaluation of an automatic speech recognition (ASR) based task for a mobile, hand\u2010held device is presented. The use of ASR on mobile devices will make environmental noise, and the design of transducers and compensation algorithms for dealing with noise, critical issues for the success of ASR based services on these devices. 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