1) There are certain operational difficulties associated with speech recognition.

2) Possible applications are continuous speech recognition and commands to robot arms.

3) The new speech recognition system is not absolutely foolproof, but it is a huge breakthrough.

4) Despite a large amount of research into automatic speech recognition the results have been unimpressive.

5) Noisy Environments: speech recognition is made difficult if interference is created by noisy machinery or extraneous conversations.

6) Areas such as vision, continuous speech recognition and synthesis, and machine learning have been hard.

7) The ability of speech recognition systems to discriminate words from acoustic information alone is not encouraging.

8) Currently available speech recognition systems impose a selection of constraints on the input to simplify the speech recognition task.

9) For speech recognition the principal operational difficulty to be faced is the interference to the acoustic signal from background noise.

10) Continuous speech recognition and synthesis are additional examples of tasks neural networks are undertaking with reasonable success.

11) A commonly used argument in favour of speech recognition is that it is the most natural communication medium.

12) An isolated - word speech recognition system based on HMM has been established.

13) In terms of the speech recognition technology, speaker independent large vocabulary continuous speech recognition has been realized, and is fighting into way from laboratory to industrial application.

14) The research on Automatic Speech Recognition has seen great achievement in the passed twenty years.

15) This paper presents an audiovisual continuous speech recognition system for noise environments.

16) Speech recognition has a wide application future. It has made a full application in dictation machine, telephone inquiry system and home application control etc.

17) A crucial issue in triphone-based continuous speech recognition is the large number of parameters to be estimated against the limited availability of training data.

18) One example is the method of recognition using template matching which is applied in both speech recognition and optical character recognition.

19) Jelinek took individual words as the states in his Markov model for speech recognition.

20) Its use has been recognised to be of value in applications such as machine translation and speech recognition.

21) What you hear will incorporate high-fidelity sound, speech synthesis, and speech recognition.

22) Our first aim was to examine the lexical access components of a number of existing speech recognition systems.

23) Indeed, in the later chapters we will show that this is impractical for any relatively unconstrained speech recognition system.

24) Writing allows private communication with the computer that is not possible with speech recognition.

25) MPEG, PCs with multiple CD - ROM drives, and speech recognition are, " absolutely breathtaking. "

26) This paper raised a method of pattern recognition, it can be used to the continuous speech recognition with reference, text understanding and data file analysis.

27) As for the phenomenon of coarticulation, which is a difficult problem in the field of speech recognition,(http:///speech recognition.html) the results in this article can be seen as a fundamental exploration.

28) In this paper, error tolerant techniques are studied for large vocabulary speaker independent Chinese continuous speech recognition and understanding systems.

29) The experiment result reveals this system has high recognition rate and fast recognition speed. So this system is an effective hardware realizing technique of automatic speech recognition device.

30) The application of Artificial Neural Networks ( ANN ) to Automatic Speech Recognition ( ASR ) is investigated in this thesis.

31) A speaker dependent, isolated word and small vocabulary embedded speech recognition system is designed and implemented.

32) But if this model is applied in speech recognition directly, it would produce the problems of rule disaster and network ratiocination invalidation.

33) A speech recognition system is designed to illustrate the twin rotor helicopter speech control function.

34) The automatic transcription, annotation and retrieval of broadcasting news requires automatic speech recognition, natural language processing and information retrieval technologies.

35) Automatic speech recognition is used more and more widely in people's life, which is categorized into continuous speech recognition and keyword spotting.

36) In order to make the speech recognition system maintain the good performance under these noise conditions, we must use various methods to enhance the robustness of system.

37) A vocabulary and grammar are combined within a speech-enabled application to define speech recognition within a reasonable range of efficiency for both the caller and the speech recognition processor.

38) For the application of large vocabulary word lists to embedded speech recognition, an efficient two-layer graph search algorithm is proposed.

39) A novel method to account for dynamic speaker characteristic properties in a speech recognition system is presented.

40) Furthermore it is very important that we use language model , syntax and accidence model in middle or big glossary continuous speech recognition.

41) The accuracy of speech recognition directly depends on accurate endpoint detection.

42) A. Net speech recognition system complete source code can be directly used by the test.

43) Experimental results show that Uyghur speech recognition based on subword can gain better recognition results.

44) It uses IBM ViaVoice (see Resources) through the JavaSpeech API to access its formant (constructed from phonemes) text-to-speech (TTS) and speech recognition.

45) Before training distribution-shared allophone models for continuous speech recognition, it is context-independent phone models that have to be trained.

46) Automatic Speech Recognition (ASR) under noisy environment is still a challenging problem.

47) After an overview of phrase recognition, this paper proposes a Chinese phrase recognition method based on continuous speech recognition algorithm and phrase-tree constraint.

48) Automatic Speech Recognition (ASR) based English pronunciation teaching system is the application of ASR in the field of English pronunciation teaching.

49) A novel model-based speaker adaptation algorithm, support speaker weighting(SSW), was proposed for rapid speaker adaptation in speech recognition systems.

50) A cr oss-word search algorithm was developed based on a two-layer lexical tree using context-dependent acoustic models for efficient continuous speech recognition .

51) Speech recognition ability of cochlear implant patients are influenced by speech encoding strategy.

52) The visual features can improve the performance of the speech recognition system under noisy environment.

53) Traditional research in speech - based technology is concentrated on speech recognition, speech synthesis and natural language understanding.

54) For speech recognition, voice data need to be cut to extract necessary voice segment from each sub-paragraph speech.

55) Recognition Crescendo Speech Processing is a comprehensive speech recognition solution designed for professional, dictation - intensive environments.

56) These high tech high jinx will keep your family bewildered as you laugh when their desktop goes haywire or their speech recognition software only responds to Klingon.

57) Recently, as an auxiliary method for Automatic Speech Recognition(ASR),(http:///speech recognition.html) computer lip-reading technology has received more and more concern.

58) SONAR both supports Automatic Speaker Recognition and Automatic Speech Recognition.

59) Experimental result shows that LOPDI algorithm can greatly increase automatic speech recognition system's robustness against additive noise.

60) The speech understanding performance is usually constrained by speech recognition errors and out - of - grammar problems.

61) Speech recognition is such a kind of technology that deals with speech signals with computer and recognizes them to symbol sequence.

62) Large vocabulary continuous speech recognition system consists of several components, and recognition errors are caused by different factors.

63) A large quantities of phonetically segmented speech is necessary in speech recognition and speech synthesis.

64) Further more, in present years, audio processing technology developed rapidly and speech recognition technology already grown up and achieve high veracity in vocabulary speech recognition.

65) The training unit is a very important part in speech recognition and it determines the entire properties of the system.

66) The performance of automatic speech recognition decreases drastically for nonnative speakers, especially those who are just beginning to learn foreign language or who have heavy accents.

67) In order the discrimination and robustness of speech recognition system, this thesis does some deeper research.

68) The main advantages of this method is operation simple and save memory, so that it may have higher speed and accuracy for speech recognition.

69) In this paper, the real-time realization of Automatic Speech Recognition (ASR) technology on common platform is investigated.

70) To the sensor , Compound Cepreum Coefficients ( CD - CC ) is proposed Linear Predication Cepreum Coefficients ( LPCC ) in the speech recognition system.

71) This paper addresses the problem of speech recognition under telephone channel conditions using data simulation method and HMM(Hidden Markov Model)adaptation.

72) The speech control robot is to use a single slice machine Be the core parts, ask for help it of speech recognition function to carry out the equipments of the control.

73) The system employs large vocabulary continuous speech recognition engine in front speech input end.

74) An error-tolerant algorithm in decoding module of Mandarin continuous speech recognition is examined to correct substitution, insertion and deletion errors in acoustic recognition.

75) Speech endpoint detection is an important process of speech analysis, speech recognition and speech monitor.

76) Compared with continuous speech recognition, keyword spotting has advantage in increasing the naturalness of the dialogue.

77) Research on speech recognition error detection and correction by natural language understanding (NLU) method will be an important research direction of improving the performance of speech recognition.

78) A speech recognition method based on modified hidden Markov model (MHMM) is presented. The weighted function algorithm is introduced to reduce the error rate of the system.

79) Speech recognition and natural language processing are technologies still in their infancy.

80) A Chinese speech recognition method based on character - based N - gram model is proposed in this thesis.

81) In Mandarin speech recognition, this model shows a better performance and requires less memory space than the word based trigram model.

82) Aiming at the application of speech recognition technology in aspect of controlling machine tool, we introduce the classes, principles and technology of speech recognition simply.

83) In this paper, the real - time realization of Automatic Speech Recognition ( ASR ) technology on common is investigated.

84) The object is to study the students and Automatic Speech Recognition to interested researchers.

85) Speech endpoint detection is a paragraph beginning and end speech analysis, speech synthesis and speech recognition of a necessary link.

86) Experiments on speaker-independent continuous speech recognition demonstrated that the combined model performed much better than both LPHMM and traditional HMM.

87) Speech pattern match is one of the key steps in speech recognition.

88) The longest established of these is automatic speech recognition (ASR),(http://) the technology that converts the spoken word to text.

89) Privacy issues aside, this practice is critical for improving the quality of speech recognition and for the continued expansion of voice application platforms.

90) This thesis presents work in the area of automatic Speech Recognition (ASR).

91) Will be studied in speech recognition (dynamic time warping) algorithm for voiceprint identification technology.

92) A noise-free PZT sensor is made as speech signal input equipment to an auto speech recognition system.

93) Language identification is a kind of technology of identifying the language of an utterance automatically by using a computer, whose development is based on speech recognition.

94) Computational linguistics, moreover, is closely related to the fields of artificial intelligence, language/speech recognition, translation, and grammar checking.

95) The core of intelligent telegraphy system is speech recognition technology.

96) Voice conversion is a new area of speech technology; the studies on it will promote the research of speech analysis, speech coding, speech synthesis, speech enhancement, speech recognition and so on.

97) The variation of pitch contour is an important parameter for speech recognition and transmission.

98) Always, the processing of the audio frequency signal is mainly concentrated on speech processing, such as speech recognition and talks identify etc.

99) Environmental robustness is a very important issue in the field of automatic speech recognition ( ASR ) research.

100) This thesis is focused on the research topic of noise-robust front-end of automatic speech recognition (ASR).

101) Only recently, for example, has continuous speech recognition for dictation become available.

102) By combining embedded system with speech recognition technique, a new type of electronic secretary mobile phone based on speech recognition is designed.

103) In this way the interactive speech recognition system may react to noise conditions that are inappropriate for generating reliable speech recognition.

104) The wavelet analysis is applying widely to pure mathematics, applied mathematics, signal processing, speech recognition and synthesis,() automation processing and image analysis etc.

105) Language identification is the process of determining the language to which a given utterance belongs by a computer, which is an important research direction in speech recognition.

106) And then, two methods of speech recognition are researched: Hidden Markov Mode1 (HMM) and Artificial Neutral Net (ANN).

107) Large vocabulary continuous speech recognition is main direction of speech recognition research at present, in which the research on particularity of tone recognition is an important work.

108) An EMIEW robot used its speech recognition and synthesized voice technology to temporarily fill the job of hotel clerk at the Grand Tokyo Bay Hotel in March of 2006.

109) The formant and corresponding bandwidth parameters result in a correct recognition rate of 98% in a small vocabulary mandarin speech recognition system.

110) FSVQ is a recallable vector quantization system, which also uses past information for optimizing the code book, and is more effective for speech recognition.

111) This paper presents a new method for speech recognition in stationary noise.

112) A fast speech recognition system for small vocabulary is presented.

113) The Minimum Mean Squared Error (MMSE) estimator for the speech feature parameters in spectrum-domain based the prior probability distribution is to enhance the correctness of speech recognition.

114) We use the theory of biomimetic pattern recognition for the speaker - independent continuous speech recognition.

115) His credentials stem from being a pioneer in various fields of computing, such as optical character recognition – the technology behind CDs – and automatic speech recognition by machine.

116) An English speaker talks into the phone. Automatic speech recognition distinguishes what is said and generates a text file that software translates to the target language.

117) Voice-to-text is a type of speech recognition program that converts spoken to written language.