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We performed speaker-independent phone recognition on the task of TIMIT [27] and speaker-dependent large vocabulary speech recognition on the task of telehealth captioning [28].
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In Section 5, we present evaluation results on the task of TIMIT phone recognition and the task of telehealth LVCSR.
With modern PACS/RIS systems using embedded voice-recognition dictation, radiologists must take on the task of proofreading and correcting their own dictation, a task many have delegated to transcriptionists in the past.
In this section, we particularly focus on the task of roadside recognition which is widely used for vision-guided vehicle navigation.
Finally, an exploratory comparison between the models on the task of continuous speech recognition was made.
The baseline system was trained on uncompressed data and tested on both the uncompressed and the compressed signals in the task of phoneme recognition and LVCSR.
Frequency and regularity of pausing behavior, based on obtained models, is currently tested in the task of automatic emotion recognition.
Recognition of the task is important.
Postoperative PD performances were significantly worse than HC and preoperative PD performances on the emotion recognition task of the Eyes test, whereas there was no significant difference between preoperative PD and HC performances.
However the tasks of teaching and professional recognition are unclear.
Experimental results on the TIMIT phone recognition task and a large vocabulary continuous speech recognition (LVCSR) task of telehealth captioning demonstrated that the proposed approach of integrating template matching with statistical modeling significantly improved recognition accuracy over the hidden Markov modeling (HMM) baselines for both TIMIT and telehealth tasks.
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