ライフサイエンスコーパス: speech recognition

1 ion cues may be a strong predictor for aided speech recognition.

2 to show the application of these devices in speech recognition.

3 hesized to have additive negative effects on speech recognition.

4 eech sounds and is behaviorally relevant for speech recognition.

5 neighborhoods may shift, adversely affecting speech recognition.

6 cular frequencies, or formants, essential in speech recognition.

7 complex forms of sensory processing, such as speech recognition.

8 ory modalities--face recognition and speaker/speech recognition.

9 ren's multitasking abilities during degraded speech recognition.

10 ask costs while multitasking during degraded speech recognition.

11 s demonstrate that individual differences in speech recognition abilities are reflected in the underl

12 istic modeling methods akin to those used in speech recognition and computational linguistics were us

13 l source separation can be applied to robust speech recognition and hearing aids and may be extended

14 Speech recognition and language learning can be affected

15 the TFS in natural speech sentences on both speech recognition and neural coding.

16 ic technologies such as machine translation, speech recognition, and speech synthesis.

17 ng human and ASR solutions to the problem of speech recognition, and suggest the potential for furthe

18 Automatic Speech Recognition (ASR) systems with near-human levels

19 ocial cognition and communication (affective speech recognition (ASR), reading the mind in the eyes,

20 ata imply that previously reported emotional speech recognition deficits in basal ganglia patients ma

21 longer-term improvements in the accuracy of speech recognition following perceptual learning resulte

22 e for normal hearing listeners and automatic speech recognition for machines.

23 eficial in many disciplines including visual speech recognition, for surgical outcome assessment in p

24 ication, but their relative contributions to speech recognition have not been fully explored.

25 reliable neural representation suitable for speech recognition, however, remains elusive.

26 Speech recognition in a single-talker masker differed on

27 ners in everyday conversations, meaning that speech recognition in conventional tests might overestim

28 The masking release (MR; i.e., better speech recognition in fluctuating compared with continuo

29 portant in acoustic communication, including speech recognition in humans.

30 Speech recognition in noise can be challenging for older

31 Speech recognition in noise was compared for cochlear im

32 ognition in quiet, FM significantly enhances speech recognition in noise, as well as speaker and tone

33 cal representation limits the performance of speech recognition in noise.

34 larly their effectiveness in improving human speech recognition in noise.

35 mber of spectral bands may be sufficient for speech recognition in quiet, FM significantly enhances s

36 for experimental and clinical assessment of speech recognition, in which good performance can arise

37 Speech recognition is remarkably robust to the listening

38 ng normal aging in humans, preserving robust speech recognition late into life.

39 onsiderable overlap in the audiograms and in speech recognition performance in the unimplanted ear be

40 In general, measures of speech recognition performance were well accounted for b

41 This research aims to bridge the gap between speech recognition processes in humans and machines, usi

42 These data, combined with more rigorous speech recognition results in older children, merit a gr

43 modulation is positively correlated with the speech recognition scores of individual subjects.

44 Speech recognition starts with representations of basic

45 Reports were then entered into the speech recognition system so that each report was associ

46 abled automatic report population within the speech recognition system.

47 e 6- and 4-channel conditions of the primary speech recognition task with decreased accuracy on the v

48 re has direct translational implications for speech recognition technology.

49 Speech recognition telephone calls to parents in the int

50 and implant experience to undergo adult-type speech recognition tests, surgical series show that thes

51 o and audio processing, computer vision, and speech recognition, their applications to three-dimensio

52 Originally developed for speech recognition, this method has been used in data mi

53 omplementary contributions to support robust speech recognition under realistic listening situations.

54 ramatically improved the state-of-the-art in speech recognition, visual object recognition, object de

55 ols, behavioral improvement in auditory-only speech recognition was based on an area typically involv

56 evealed that FM is particularly critical for speech recognition with a competing voice and is indepen

57 Each circuit markedly improved speech recognition, with greater improvement observed fo