Department of CSE, IGCE Abhipur, Punjab, India 2. D epartment of Mathematics, COE/CGC Landran, Punjab, India

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1 Volume 6, Issue, January 06 ISSN: 77 8X International Journal of Avance Research in Comuter Science an Software Engineering Research aer Available online at: A Stuy of Feature Extraction for Automatic Seech Recognition Bhuiner Singh, Dr. Joginer Singh Deartment of CSE, ICE Abhiur, unjab, Inia D eartment of Mathematics, COE/CC Lanran, unjab, Inia Abstract: In the Era of igital signal rocessing technology has le the use of seech rocessing in many ifferent alication areas like seech comression, enhancement, synthesis, an recognition. In this aer the issue of seech recognition an role of feature extraction has been stuie in etail. This stuy inclues seech feature extraction using Linear reictive Coefficients, Cestral analysis an then Vector quantization of isolate-value seech feature is one. Keywors: Automatic Seech Recognition (ASR), Linear reictive Coing (LC), Vectors Quantization (VQ). I. INTRODUCTION Automatic seech recognition by comuters is a rocess where seech signals are automatically converte into the corresoning sequence of wors in text. With recent avances, seech recognizers base uon Hien Markov Moels (HMM s) have achieve a high level of erformance in controlle environment. In real life alications, however, seech recognizers are use in averse environments. The recognition erformance is tyically egrae if the training an the testing environments are not the same. The goal of Automatic Seech Recognition is to evelo techniques an systems that enable comuters to accet seech inut. The seech recognition roblem may be interrete as a seech-to-text conversion roblem. Users want their voices, seech signals in to be transcribe into text by a comuter. II. FEATURE EXTRACTION Feature extraction is the rocess of retaining useful information of the signal while iscaring reunant an unwante information. However, in ractice, while removing the unwante information, on may also lose some useful information in the rocess. Feature extraction may also involve transforming the signal into a form aroriate for the moels use for classification. In eveloing an ASR system, a few esirable roerties of the features are: High iscrimination between sub-wor classes. Low Seaker variability. Invariance to egraations in the seech signal ue to channel an noise. The goal is to fin a set of roerties of an utterance that have acoustic correlates in the seech signal, that is, arameters that can somehow be comute or estimate through rocessing of the signal waveform,. Such arameters are terme features. Next ste after the rerocessing of the seech signal in the signal moeling is feature extraction. Feature extraction is the arameterization of the seech signal. This is intene to rouce a ercetually meaningful reresentation of the seech signal. Feature extraction tyically inclues the rocess of converting the signal to a igital from (i.e. signal conitioning), measuring some imortant characters of the signal such as energy or frequency resonse (i.e. signal measurement), augmenting these measurements with some ercetually-meaningful erive measurements (i.e. signal arameterizatio an statistically conitioning these numbers to form observation vectors. The objective with feature extraction to attaine are: To untangle the seech signal into various acoustically ientifiable comonents. To obtain a set of features with low rates of change in orer to kee comutations feasible. Feature extraction can be subivie into three basic oerations: sectral analysis, arametric transformation an statistical moeling (Becchetti an Ricotti, 004). The comlete sequence of stes is summarize in figure.. Figure. : An overview of the Feature Extraction rocess A. Sectral Analysis: When seech is rouce in the sense of time varying signal, its characteristics can be reresente via arameterization of the sectral activity. There are six major classes of sectral of analysis algorithms i.e. Digital filter bank (ower 06, IJARCSSE All Rights Reserve age 64

2 Bhuiner et al., International Journal of Avance Research in Comuter Science an Software Engineering 6(), January - 06, estimatio, Fourier Transform (FT Derive Filter Bank Amlitues, FT Derive Cestral Coefficients), Linear reiction (L, L Derive Filer Bank Amlitues, L Derive Cestral Coefficients) use in seech recognition system. From these classes, linear reiction gives best results. Tyes of Linear reiction are exlaine as below: ( LC (LC analysis): Linear reictive Coing (LC) has been oular for seech comression, synthesis an as well as recognition since its introuction in the 960s because it offers a reasonable engineering aroach for seech signal analysis. Linear reiction moels the human vocal tract as an infinite imulse resonse (IIR) system rouces the seech signal. Linear reictive Coing (LC) is a very imortant sectral estimation technique because it can rovie an estimate of the oles (hence the formants) of the vocal tract transfer function. The LC algorithm is a th orer linear reictor which attemts to reict the value of any oint in a time-various linear system base on the values of the revious samles. The all-ole reresentation of the vocal tract transfer function, H(z) can be reresente by the following equation: H ( z) a z a z a z A( z)... The values a( are calle the reiction coefficients while reresents the amlitue or gain associate with the vocal tract excitation. The oles of the transfer function in equation are etermine by the roots of the olynomial in the enominator. Because the LC moel is an all ole moel, it can cature the resonant frequencies, or formants, but not the zeros, which are imortant for nasalize souns. In aition, LC oes not aequately estimate signals which have no oles, such as some unvoice seech an noise. For the seech signal rouce by a linear system, the reicate seech samle ŝ( is a function of a( an rior seech samles accoring to: ŝ(= a( n i LC analysis involves solving for the a( terms accoring to a lest error criterion. If the error is efine as: e( =- ŝ( = a( n i Then, taking the erivative of the square error with resect to the coefficients a(j) an setting it equal to zero gives: ( a( n )) 0 a( j) i thus, n-j)= a( n j) for j,..., i There are tow rincial methos for solving above equation for the reiction coefficients a(. The first is an auto correlation metho, which multilies the seech signal by a Hamming winow or similar time winow, assuming that the seech signal is stationary within an zero outsie, the analysis winow. The autocorrelation solution to equation can be exresse as R(j)= a( R( i j ) j,..., i where, R(j) is an even function an is comute from: N j R(j)= m) m j) j,..., m0 where, γ is a normalization factor. Once the autocorrelation terms R(j) have been calculate, a recursive algorithm name Levinson-Durbin Algorithm us use to etermine the values of a(. An alternative metho for etermining the LC coefficients calle the covariance metho is a irect Cholesky ecomosition solution of the following equation. R(j)=a(R( i-j ) This equation can be exresse in matrix form. Unlike autocorrelation metho, it oes not use a winow to force the samle outsie the analysis interval to zero. Thus, the limits on the comutation of R(j) exten from n N -. (i L-erive filter bank amlitues: Linear reiction erive filter bank amlitues are efine as filter bank amlitues resulting from samling the L sectral moel (rather than the signal sectrum) at the aroriate filter bank frequencies. Now the question is how can one efficiently samle the sectrum given the L moel? A straightforwar technique to comuter filter bank amlitues from the L moel involves irect evaluation of the L moel. The sectrum is tyically over samle an average estimates are generate for actual filter bank amlitues. (ii L-erive cestral coefficients: IN the last section, the L moel is leverage to comute L-erive filter bank amlitues. Another logical ste in this irection woul be to use the L moel to comuter cestral coefficients. If the Linear reiction filter is stable (an it is guarantee to be stable in the autocorrelation analysis), the logarithm of the inverse filter can be exresse as a ower series in z -. 06, IJARCSSE All Rights Reserve age 65

3 Bhuiner et al., International Journal of Avance Research in Comuter Science an Software Engineering 6(), January - 06, C L ( z) N c i0 C L ( z log H( z) log( It can solve for the coefficients by ifferentiating both sies of the exression with resect to z - an equating coefficients of the resulting olynomials. This results in the following recursion. Initialization C L (0) = log = 0, C L () = a L () i For j i Nc, CL ( al ( ( ) al ( j) CL ( i j) j i The coefficients C L are referre to as L-erive Cestral Coefficients. Historically, C L (0) has been efine as the log of the ower of the L error. For now, it is note that since ower will be ealt with as a searate arameter, there is no nee to inclue it in the equations above. It can regar the Cestral moel, in which C L (0) = log =0. The number of Cestral coefficients comute is usually comarable to the number of L coefficients: 0.75 N C.5. The cestral coefficients comute with the recursion escribe above reflect a linear frequency scale. One rawback to the L-erive cestral coefficients is that it must work a little harer to introuce the notion of a nonlinear frequency scale. The referre aroach is base on a metho use to war frequencies in igital filter esign. B. arameter Transforms: Signal arameters are generate from signal measurements through two funamental oerations: ifferentiation an concatenation. The outut of this stage of rocessing is a arameter vector containing our raw estimates of the signal. ( Differentiation: To better characterize temoral variations in the signal, higher orer time erivatives of the signal moel. The absolute measurements reviously iscusse can be though of as Zero th orer erivatives. In igital signal rocessing, there are several ways in which a first-orer time erivative can be aroximate. Three oular aroximations are: S ( n ) t S ( n ) t Na S ( m n m) t mn The first two equations are known as backwar an forwar ifferences resectively. The first equation is same as reemhasis filter. The thir equation reresents a linear hase filter aroximation to an ieal ifferentiator. This is often referre to as regression analysis. The signal outut from this ifferentiation rocess is enote as elta arameter. The secon-orer time erivative can be similarly aroximate by realying thir equation again to the outut of the first orer ifferentiator. This outut is often referre to as a elta-elta arameter. Obviously, it can exten this rocess to higher other erivatives. (i Concatenation: Most systems ost rocess the measurements in such a way that the oerations can be easily exlaine in terms of linear filtering theory. Here this notion is generalize in the form of a matrix oerator. For research uroses, it is convenient to view the signal moel as a matrix of measurements. The signal measurement matrix usually contains a mixture of measurements: ower an a set of cestral coefficients. The concatenation is the creation of a single arameter vector er frame that contains all esire signal arameters. Some arameters such as ower, are often normalize before the comutation. It is common to simly ivie the ower by the maximum value observe over an utterance (or subtract the log of the ower). With the emergence of Markov moeling techniques that rovie a mathematical basis for characterizing sequential (or temoral) asects of the signal, the reliance uon ynamic features has grown. Toay, ynamic features are consiere essential to eveloing a goo honetic recognition caability because rai change in the sectrum is a major cue in classification of a honetic-level unit. C. Statistical Moeling: The thir ste of the feature extraction rocess is Statistical Moeling. Here, it assumes that the signal arameters were generate from some unerlying multivariate ranom rocess. To learn or iscover the nature of this rocess, it imose a moel on the ata, otimize (or trai the moel, an then measure the quality of the aroximation. The only information about the rocess is its observe oututs, the signal arameters that have been comute. For this reason, the arameter vector outut from this stage of rocessing is often calle the signal observations. A statistical analysis is to be erforme on the vectors to etermine if they are art of a soken wor or hrase or whether they are merely noise. Seech souns such as the ah soun in the father exhibit several resonance in the sectrum that tyically exten for 0ms. Transitional souns, such as the b in boy exist for a brief interval of aroximately 0 ms. Statistical moel in seech recognition is shown in figure.. 06, IJARCSSE All Rights Reserve age 66 N L j0 a L L ( j) z )

4 Bhuiner et al., International Journal of Avance Research in Comuter Science an Software Engineering 6(), January - 06, Figure.: Statistical Moels in Seech Recognition Seech recognition system use extremely sohisticate statistical moel, as this is one of the funamental functions of a seech recognizer. Vector Quantization (VQ) has been useful in a wie variety of seech rocessing alications an forms the basis for the more sohisticate algorithm. This basic concet of VQ alie to seech comression is schematically eicte in figure.3. A training seech sequence is first use to generate the coebook. The seech is segmente (winowe) into successive short frames an a vector of finite imensionality reresents each frame of seech. The vector may be in form of samle ata, FFT coefficients, autocorrelation terms, or their transformations (linear or non-linear). Coebook generation requires an iterative rocess much like a clustering algorithm involving a large number of sectral moel vectors (coebook) so that the average sectral istortion from all the inut vectors to the same sectral comression strategy in the coebook generation rocess is execute in the quantizer. Each inut vector is mae to the coebook entry (coe-wor) inex corresoning to the best match vector. Seech comression or rate reuction is accomlishe by using the inexes as storage or transmission arameters. For Vector Quantization, it is necessary to have a measurement of issimilarity between the two vectors. Distortion measures base uon transformation, which retain only the smoothe behavior of the seech signal, have been alie in seech recognition, seaker ientification an verification tasks. Figure.3 : Vectors Quantization Training an Classification Structure To buil a VQ coebook an imlement a VQ analysis roceure, one nees the following: A larger set of sectral vectors, {xj; j=0,,n-}, which form a training set. The training set is use to create the otimal set of the coebook vectors for reresenting the sectral variability observe in the training set. A istance measure between a air of sectral analysis, so as to able to cluster the training set vectors as well as to classify arbitrary sectral vectors into unique coebook entries. A centroi comutation roceure: On the basis of the artitioning that classifies the training vectors into the M clusters, choose the M coebook vectors as the centroi of each of the M clusters. A classification roceure for arbitrary seech sectral analysis vectors that choose coebook vector closest to the inut vector an uses the coebook inex as the resulting sectral reresentation. III. CONCLUSION In this aer we have stuie features extraction techniques use for Seech Recognition, all techniques an roerties are iscusse. During stuy we foun the imortance of feature extraction for the eveloment Automatic Seech Recognition System (ASR) an a few esirable roerties of the features are also iscusse like : High iscrimination between sub-wor classes, Low Seaker variability an Invariance to egraations in the seech signal ue to channel an noise. Basic oeration sectral analysis, arametric transformation an statistical moeling of feature extraction are also iscusse.this aer will hel the researchers who willing to work in the area of seech recognition know the basic about feature extraction techniques REFERENCES [] Abulla, W. (00), HMM base techniques for seech segment extraction, Scientific rogramming, IOS ress, Amesteram, The Netherlans, Vol. 0, Issue 3,. 39. [] AbulKair K, (00), Recognition of Human Seech using q-bernstein olynominals, International Journal of Comuter Alication, Vol. No. 5,. -8. [3] Akhuutra, V., Jitaunkul, S., ornsukchanra, W. an Luksaneeyanawin, S. (997), A seaker-ineenent Thai olysyllabic wor recognition using Hien Markov Moel, in roceeings of IEEE acific Rim Conference on Communications, Comuters an Signal rocessing, Vol., [4] Anusuya an Katti (009), Seech Recognition by Machine: A Review, International Journal of Comuter Science an Information Security, Vol. 6, No. 3, [5] Atal, Bishnu S. an Rabiner, Lawrence R. (976), A attern Recognition Aroach to Voice- Unvoice Classificaton with Alication to Seech Recognition, in roceeings of the IEEE International Conference on Acoustic, Seech an Signal rocessing (ICASS 76), ennsylvania, Vol. 4, No. 3, , IJARCSSE All Rights Reserve age 67

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