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rig. /. Linear predictive model or speech{2}| 5] The interpretation of above Eq. is given in Fig., which shows the non linearized excitation source, u(n),being scaled by the gain, G, and acting as input to the all-pole system, H(z) =

Figure 7 rig. /. Linear predictive model or speech{2}| 5] The interpretation of above Eq. is given in Fig., which shows the non linearized excitation source, u(n),being scaled by the gain, G, and acting as input to the all-pole system, H(z) =

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4.Lf LACOUSLICKE MONMELLC AAP PIOUCHES LY OPEeCHl NCCOSILLLLOIL Fig. 1 shows a block diagram of the acoustic-phonetic approach to speech recognition. The first step in the processing (a step common to all approaches to speech recognition) is the speech analysis system (the so-called feature measurement method), which provides an appropriate (spectral) representation of the characteristics of the time- varying speech signal[2][4]. The most common techniques of spectral analysis are the class of filter bank methods and the class of linear predictive coding (LPC) methods. The next step in the processing is the feature-detection stage. The idea here is to convert the spectral measurements to a set of features that describe the broad acoustic properties of the different phonetic units. The third step in the procedure is the segmentation and labeling phase whereby the system tries to find stable regions (where the features change very little over the region) and then to label the segmented region according to how well the features within that region match those of individual phonetic units[2]. Fig. 1. Block diagram of Acoustic-Phonetic Approach to Speech Recognition rowed
4.Lf LACOUSLICKE MONMELLC AAP PIOUCHES LY OPEeCHl NCCOSILLLLOIL Fig. 1 shows a block diagram of the acoustic-phonetic approach to speech recognition. The first step in the processing (a step common to all approaches to speech recognition) is the speech analysis system (the so-called feature measurement method), which provides an appropriate (spectral) representation of the characteristics of the time- varying speech signal[2][4]. The most common techniques of spectral analysis are the class of filter bank methods and the class of linear predictive coding (LPC) methods. The next step in the processing is the feature-detection stage. The idea here is to convert the spectral measurements to a set of features that describe the broad acoustic properties of the different phonetic units. The third step in the procedure is the segmentation and labeling phase whereby the system tries to find stable regions (where the features change very little over the region) and then to label the segmented region according to how well the features within that region match those of individual phonetic units[2]. Fig. 1. Block diagram of Acoustic-Phonetic Approach to Speech Recognition rowed
Fig. 2. Block diagram of pattern-recognition speech recognizer [3]
Fig. 2. Block diagram of pattern-recognition speech recognizer [3]
A third alternative is the so-called blackboard approach, as illustrated in fig.. In this approach, all knowledge sources (KS) are considered independent; a hypothesis and- test paradigm serves as the basic medium of communication among KSs; each KS is data driven, based on the occurrence of patterns on the blackboard that match the templates specified by the KS; the system activity operates asynchronously; assigned cost and utility considerations and an overall ratings policy to combine and propagate ratings across all levels. Fig. 3. Illustration of word correcting capability of syntax in speech recognition [3].
A third alternative is the so-called blackboard approach, as illustrated in fig.. In this approach, all knowledge sources (KS) are considered independent; a hypothesis and- test paradigm serves as the basic medium of communication among KSs; each KS is data driven, based on the occurrence of patterns on the blackboard that match the templates specified by the KS; the system activity operates asynchronously; assigned cost and utility considerations and an overall ratings policy to combine and propagate ratings across all levels. Fig. 3. Illustration of word correcting capability of syntax in speech recognition [3].
Fig. 4. A bottom up approach to knowledge integration for speech recognition [3].
Fig. 4. A bottom up approach to knowledge integration for speech recognition [3].
Fig. 5. A top down approach to knowledge integration for speech recognition[3]
Fig. 5. A top down approach to knowledge integration for speech recognition[3]
Fig. 6. A blackboard approach to knowledge integration for speech recognition [3][4]
Fig. 6. A blackboard approach to knowledge integration for speech recognition [3][4]
These parameters are all vary slowly with time. Fig. 8. Speech synthesis model based on LPC model[4] the gain parameter, and the coefficients of digital filters{ a, }. corresponding to the LFC analysis. Here the normalized excitation source is chosen by a switch whose position is controlled by the voiced/unvoiced character of the speech, which chooses either a quasi periodic train of pulses as the excitation for voiced sounds, or a random noise sequence for unvoiced sounds. The appropriate gain, G, of the source is estimated from the speech signal, and the scaled source is used as input to a digital filter (H(z)), which is controlled by the vocal tract parameters characteristic of the speech being produced. Thus the parameters of this model are voiced/unvoiced classification, pitch period for voiced sounds,
These parameters are all vary slowly with time. Fig. 8. Speech synthesis model based on LPC model[4] the gain parameter, and the coefficients of digital filters{ a, }. corresponding to the LFC analysis. Here the normalized excitation source is chosen by a switch whose position is controlled by the voiced/unvoiced character of the speech, which chooses either a quasi periodic train of pulses as the excitation for voiced sounds, or a random noise sequence for unvoiced sounds. The appropriate gain, G, of the source is estimated from the speech signal, and the scaled source is used as input to a digital filter (H(z)), which is controlled by the vocal tract parameters characteristic of the speech being produced. Thus the parameters of this model are voiced/unvoiced classification, pitch period for voiced sounds,
The recognizer matches the sequence of observations with subword models or with word models which are built from subword models with predefined word lexicon. The recognized word or subword units are used to construct the whole sentence with the help of language models. It is common for a continuous speech recognition system to recognize the whole sentence by first constructing a finite- state network, with or without a grammar based language model, to represent the possible sentence model. Followins diasram shows our input sional.
The recognizer matches the sequence of observations with subword models or with word models which are built from subword models with predefined word lexicon. The recognized word or subword units are used to construct the whole sentence with the help of language models. It is common for a continuous speech recognition system to recognize the whole sentence by first constructing a finite- state network, with or without a grammar based language model, to represent the possible sentence model. Followins diasram shows our input sional.
Pig. 7. Dasic principle Of speeck recognizer. Most ASR systems consist of three major modules i.e. signal processing front-end, acoustic modeling and language modeling. The signal processing front-end transforms the speech signal into a sequence of feature vectors to be used for classification. Generally, this representation has a considerably lower information rate than the original speech waveform|3 ]. All ASR systems operate in two phases. First, a training phase, during which the system learns the reference patterns representing the different speech sounds (e.g. phrases, words, phones) that constitute the vocabulary of the application. Each reference is learned from spoken examples and stored either in the form of templates obtained by some averaging method or models that characterize the statistical properties of pattern. Second, a recognizing phase, during which an unknown input pattern, is identified by considering the set of references.
Pig. 7. Dasic principle Of speeck recognizer. Most ASR systems consist of three major modules i.e. signal processing front-end, acoustic modeling and language modeling. The signal processing front-end transforms the speech signal into a sequence of feature vectors to be used for classification. Generally, this representation has a considerably lower information rate than the original speech waveform|3 ]. All ASR systems operate in two phases. First, a training phase, during which the system learns the reference patterns representing the different speech sounds (e.g. phrases, words, phones) that constitute the vocabulary of the application. Each reference is learned from spoken examples and stored either in the form of templates obtained by some averaging method or models that characterize the statistical properties of pattern. Second, a recognizing phase, during which an unknown input pattern, is identified by considering the set of references.
Fig. 12. Average zero crossing rates of speech signal. w(n) = 1/2N, 0 <= n <= N-1 This measure could allow the discrimination between voiced and unvoiced regions of speech, or between speech and silence. Unvoiced speech has in general, higher zero-crossing rate. The signals in the graphs are normalized.
Fig. 12. Average zero crossing rates of speech signal. w(n) = 1/2N, 0 <= n <= N-1 This measure could allow the discrimination between voiced and unvoiced regions of speech, or between speech and silence. Unvoiced speech has in general, higher zero-crossing rate. The signals in the graphs are normalized.
This measurement can in a way distinguish between voiced and unvoiced speech segments, since unvoiced speech has significantly smaller short- time energy. For the length of the window a practical choice is 10-20 msec that is 160-320 samples for sampling frequency 16kHz..This way the window will include a suitable number of pitch periods so that the result will be neither too smooth, nor too detailed.
This measurement can in a way distinguish between voiced and unvoiced speech segments, since unvoiced speech has significantly smaller short- time energy. For the length of the window a practical choice is 10-20 msec that is 160-320 samples for sampling frequency 16kHz..This way the window will include a suitable number of pitch periods so that the result will be neither too smooth, nor too detailed.
Fig. 14. (a,b) Signal after pre-emphasis,Pre-emphasis filter curve. 4.1.1.3.b Blocking and Windowing
Fig. 14. (a,b) Signal after pre-emphasis,Pre-emphasis filter curve. 4.1.1.3.b Blocking and Windowing
Related topics:
Statistical Pattern RecognitionDigital FiltersSpeech Production Models
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