Framework for Speech Enhancement and Recognition

Framework for Speech Enhancement and Recognition A Generalized Framework for Speech Enhancement and Recognition with Special direction On Patients with Speech DisordersLiterature ReviewKumara Sharma et.al. pay back proposed Harmonics-to-Noise Ratio and Critical-Band Energy Spectrum of talk as Acoustic Indicators of Laryngeal and Voice Pathology 8. Acoustic analysis of talking to points is a noninvasive proficiency that has been proved to be an effective tool for the objective support of birdcall and component part disease screening. In the present get hold of acoustic analysis of sustained vowels is considered. A simple k-means nearest neighbor classifier is designed to test the efficacy of a harmonics-to- disruption ratio (HNR) measure and the critical-band energy spectrum of the piano rescue signal as tools for the detection of laryngeal pathologies 12. It groups the given voice signal sample into pathologic and pattern. The easy lecturing signal is decomposed into harm onic and note dowerys utilise an iterative signal extrapolation algorithm. The HNRs at quadruple different frequency bands are estimated and used as features. Voiced savoir-faire is also pick uped with 21 critical-band come down filters that mimic the human audile neurons. Normalized energies of these filter outputs are used as another set of features. The HNR and the critical-band energy spectrum bay window be used to correlate laryngeal pathology and voice alteration, using previously classified voice samples. This method could be an additional acoustic indicator that supplements the clinical diagnostic features for voice evaluation 42.Cepstral-based appraisal is used to provide a baseline estimate of the hoo-hah level in the logarithmic spectrum for voiced speech. A theoretical description of Cepstral treat of voiced speech containing inspiration disruption, together with supporting empirical data, is provided in order to illustrate the nature of the noise baseline e stimation process. Taking the Fourier transform of the liftered (filtered in the Cepstral domain) cepstrum produces a noise baseline estimate. It is shown that Fourier transforming the low-pass liftered cepstrum is comparable to applying a moving average (MA) filter to the logarithmic spectrum and hence the baseline receives contributions from the glottal source arouse vocal tract and the noise excited vocal tract43. Because the estimation process resembles the action of a MA filter, the resulting noise baseline is determined by the harmonic dissolver as determined by the temporal analysis window length and the glottal source spectral tilt. On selecting an appropriate temporal analysis window length the estimated baseline is shown to lie halfway between the glottal excited vocal tract and the noise excited vocal tract. This information is employed in a new harmonics-to-noise (HNR) estimation technique, which is shown to provide accurate HNR estimates when tested on synthetically generated voice signals. HNR is defined as the ratio between the energy of the occasional(a) voice to the energy of the aperiodic component in the signal. As such it is sensitive to all forms of waveform aperiodicity 8,12. It only specifically reflects a signal to aspiration noise ratio when other aperiodicities in the signal are comparatively low. Validation of a HNR method requires testing the technique against synthesis data with a priori knowledge of the HNR.Time-domain methods that require individual period detection for HNR estimation can be problematic because of the difficulty in estimating the period markers for pathological voiced speech. frequence domain methods encounter the problem of estimating noise at harmonic locations .Cepstral techniques have been introduced to supply noise estimates at all frequency locations in the spectrum (the Cepstral affect removes the harmonics from the spectrum).It is shown that the cepstrum-based noise baseline estimation process is c omparable to applying a moving average MA filter to the power spectrum and hence the baseline receives contributions from the glottal source excited vocal tract and the noise excited vocal tract. Two important issues need to be considered with respect to HNR estimation for sustained vowel phonation when inferring glottal noise levels HNR is a global indicator of voice periodicity.HNR is indirectly tie in to the noise level of the glottal source .HNR provides a global estimate of signal periodicity. Hence a low value of HNR can arise from any form of aperiodicity, for example, from aspiration noise, jitter, shimmer, nonstationarity of the vocal tract, or other waveform anomalies 43.Daryush Mehta has discussed about uptake Noise during Phonation Synthesis, Analysis, and Pitch-Scale Modification. The current study investigates the synthesis and analysis of aspiration noise in synthesized and spoken vowels. Based on the linear source-filter model of speech production, author has imple mented a vowel synthesizer in which the aspiration noise source is temporally modulated by the periodic source waveform. Modulations in the noise source waveform and their synchronism with the periodic source are shown to be salient for natural-sounding vowel synthesis. The accurate estimation of the aspiration noise component that contains energy across the frequency spectrum and temporal characteristics due to modulations in the noise source was a challenging task for the author. ghostlike harmonic/noise component analysis of spoken vowels shows evidence of noise modulations with peaks in the estimated noise source component synchronous with both the open phase of the periodic source and with epoch instants of glottal closure 39.Due to natural modulations in the aspiration noise source, author has developed an alternate approach to the speech signal processing with the stimulate of accurate pitch-scale modification. The proposed strategy takes a dual processing approach, in whi ch the periodic and noise components of the speech signal are separately analyzed, special, and re-synthesized. The periodic component is modified using our implementation of time-domain pitch-synchronous overlap-add, and the noise component is handled by modifying characteristics of its source waveform. Author has modeled an inherent coupling between the original periodic and aspiration noise sources the modification algorithm is designed to preserve the synchronism between temporal modulations of the two sources 44. The reconstructed modified signal is perceived to be natural-sounding and generally reduces artifacts. Arpit Mathur et.al. have discussed about the significance of parametric spectral ratio methods in detection and actualization of whispered speech 45.Other ReferencesKaladhar developed confusion matrix which is a matrix for a two-class classifier, contains information about actual and predicted smorgasbords done by a classification system. The accuracy obtained by tr aining the probabilistic neural net profit using Parkinson disease dataset got 100% as positives, predictions that an instance is positive, using WEKA 3 and Matlab v7. The data explored in this research was obtained from the Oxford Parkinsons Disease Detection Dataset. Data exploit is the process of extracting patterns from data. Data mining is an important tool to transform this data into information. Authors present results with accuracy obtained by training the probabilistic neural network using the above dataset 46. Xiao Li et.al. proposed a technique to reduce the likelihood count in ASR systems that use continuous density HMMs. Based on the nature of high-octane features and the numerical properties of Gaussian mixture distributions, the mirror image likelihood computation is approximated to achieve a speedup. Although the technique does not show appreciable benefit in an isolated enounce task, it yields significant improvements in continuous speech recognition. For exa mple, 50% of the computation can be saved on the TIMIT database with only a negligible humiliation in system performance 47.Authors analyze the case with only static features and their deltas and focus on achieving computational saving by partially computing the observation probability in a Gaussian component. It ignores computing the dynamic-feature part of an observation vector when its static-feature part already falls in the tail of a Gaussian. This technique doesnt require a complicated training procedure and brings almost no overhead to the decoding process. It is effective on both isolated word and affiliated word speech tasks, but works especially well on connected word recognition with high-dimensional dynamic features 47. Elisabeth Ahlsn has discussed different types of communication disorders. In case of Global aphasia there is nil or almost no linguistic communication. In case of Brocas aphasia there is slow, effortful speech, telegram style, word finding problems know n as anomia, relatively good comprehension. In case of Wernickes aphasia there is fluent verbose speech, word finding difficulties known as anomia, substitutions of words and sounds, impaired comprehension. In case of Anomic aphasia there are only word finding problems 49.Kristen Jacobson explains about auditory and quarrel processing disorders as follows. There are three general levels that speech sounds travel through while we are listening. The first level refers to the reception of sounds that occurs within our ears. A person who is diagnosed with a hearing impairment has difficulties perceiving sounds at this level. This problem is not referred to as a processing disorder. Central auditory processing disorders (CAPD) refer to difficulties discriminating, identifying and retaining sounds afterward the ears have heard the sounds. Individuals who experience difficulties attaching meaning to sound groups that form words, sentences and stories are often diagnosed with language pro cessing disorders. They may also experience similar difficulties processing and organizing language for meaning during reading. Similar sounding words are often confused and some individuals may experience sensitivity to specific sounds. Reduced recognition of stress patterns and word boundaries within sentences is often present, especially during rapid speech or listening without visual cues. At times, only parts of messages are received accurately, so that messages and directions often appear incomplete. Specific language processing deficits are often reflected in delayed responses, the need to rehearse statements, and/or the need for haunt reviews while learning new information 50.There are various types of speech disorders in children described as follows.Articulation There is difficulty in the production of individual or sequenced sounds. The speakers exhibit substitutions, omissions, additions, and distortions of syllables or words. The Motor or Neurogenic speech disorders re sult into speech difficulties and affect the planning, coordination, timing, and execution of speech movements. Apraxia of speech is neurogenic motor speech disorder affecting the planning of speech. There is difficulty with the voluntary, purposeful movement of speech .The causes are stroke, tumor, head injury, and developmental disorders. The speakers can produce individual sounds but cannot produce them in longer words or sentences. Voice disorders affect pitch, duration, intensity, resonance, and vocal quality parameters. Fluency disorders produce interruptions in the flow of speaking. It is also known as stuttering. It means frequent repetition and/or prolongation of words or sounds 51.Treatment of children with Speech Oral status Disorders (OPD)s needs various types of speech oral placement therapy (OPT) .Children with speech OPDs may have typical or a typical oral structures. The key to the description of OPD lies in the childs ability or inability to imitate auditory-visua l stimuli and follow verbal oral placement instructions. Children with OPD cannot imitate targeted speech sounds using auditory and visual stimuli .They also cannot follow specific instructions to produce targeted speech sounds 52.Thomas Dubuisson et.al. described an analysis system aiming at discriminating between normal and pathological voices. Based on the normal and pathological samples included the MEEI database, it has been found that using two features (spectral decrease and first spectral tristimuli in the Bark scale). music Information Retrieval (MIR) aims at extracting information from music in order to build classification system of music. Temporal Domain features are Energy, mean, standard deviation. Spectral features are spectral Delta, Spectral Mean Value, Spectral Standard Deviation, Spectral Center of Gravity known as spectral centroid, Spectral Moments. The first four moments of the power spectrum M1, M2, M3, M4 . M3 is used to compute the skewness defining the ori entation of the PSD around its first moment. If it is positive, the PSD is more oriented to the right and to the left if is negative. The skewness is computed as lopsidedness = M3/(M2)3/2 . The fourth moment is used to compute the kurtosis defining the acuity of the PSD around its first moment. A Gaussian distribution is having a kurtosis equal to 3, a distribution with a higher kurtosis is more acute than a Gaussian one while a distribution with a lower kurtosis is more now than a Gaussian distribution. The kurtosis is computed asKurtosis = M4/(M2)2. The Soft Phonation Index is defined for the (01000 Hz) and (08000 Hz) frequency bands 54. Behnaz Ghoraani et.al. proposed a novel methodology for automatic pattern classification of pathological voices. The main contribution of this paper is extraction of meaning(prenominal) and unique features using Adaptive time-frequency distribution (TFD) and nonnegative matrix factorization (NMF). The proposed method extracts meaningful and uniq ue features from the joint TFD of the speech, and automatically identifies and measures the abnormality of the signal. The proposed method is applied on the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database. As a payoff of fact from the TFD of abnormal speech it is evident that there are more transients in the abnormal signals, and the formants in pathological speech are more crack and are less structured 55.Corinne Fredouille et.al. have addressed voice disorder assessment. The goal of this methodology is to bring a better misgiving of acoustic phenomena related to dysphonia. The automatic system was validated on dysphonic corpus (80) fe mannish voices. These observations led to a manual analysis of unvoiced plosives, which highlighted a lengthening of VOT according to the dysphonia bad weather validated by a preliminary statistical analysis. The feature vectors issued from this analysis, at a 10 milli indorse rate, are finally normalized to fit a 0-mean and 1 -variance distribution. The LFSC/MFSC computation is done by using the (GPL) SPRO toolkit. Finally, the feature vectors can be augmented by adding dynamic information representing the way these vectors vary in time. Here, first and second derivatives of static coefficients are considered (also named and coefficients) resulting in 72 coefficients 56.Younggwan Kim et.al. discussed the role of the statistical model-based voice activity detector (SMVAD) to detect speech regions from input signals using the statistical models of noise and noisy speech. The LRT-based decision rule may cause detection errors because of statistical properties of noise and speech signals57.Wiqas Ghai et.al. described automatic speech recognition system as comprised of modules Speech Signal acquisition ,Feature extraction, using MFCC is done . Acoustic Modeling is done for expected phonetics of the hypothesis word/sentence. For generating mapping between the basic speech units such as phones, tri-phones sy llables, a rigorous training is carried. During training, a pattern representative for the features of a class using one or more patterns match to speech sounds of the same class. Language Lexical Modeling is done with the help of Text Corpus, Pronunciation Dictionary and Language Model 59.Lucas Leon Oller presents analysis of voice signals for the Harmonics-to-Noise crossbreeding frequency .The harmonics-to-noise ratio (HNR) has been used to assess the behavior of the vocal fold closure. The objective is to find a particular harmonics-to-noise crossover frequency (HNF) where the harmonic components of the voice drop below the noise floor, and use it as an indicator of the vocal fold insufficiency. . As the range used for the calculation of the cepstrum approaches the lowest octaves, the growth of the rahmonics should revivify at some point, the range is going to contain harmonics that are above the noise floor level, and then the energy of the rahmonics will start to faster. Th at point would be the harmonics-to-noise crossover frequency 60. Daryl Ning has developed an Isolated Word Recognition System in MATLAB. A robust speech-recognition system combines accuracy of identification with the ability to filter out noise and adapt to other acoustic conditions, such as the speakers speech rate and accent. It requires detailed knowledge of signal processing and statistical modeling 61.Phonetic ConceptsDaniel Jurafsky et.al. presented a case study of Star trek where robots converse with humans in natural Dialogue system with language conversational agents. Various components that make up modern conversational agents, including language input and language output dialogue ,automatic speech recognition, natural language understanding ,response planning , speech synthesis systems and the goal of machine translation which leads to automatic translation of a document from one language to another is explained here 62.Steven Pruett describes speech as the motor act of c ommunicating by articulating verbal expression and Language as the knowledge of a symbol system used for interpersonal communication. Mary Planchart has explained four domains of language namely Phonology, Grammar , Morphology ,Syntax , and Pragmatics 63, 64.Eric J. Hunter has presented a case study of a 5 year old healthy male child. He has analyzed comparison of the childs fundamental frequencies in structured elicited vocalizations versus unstructured natural vocalizations. The child also wore a discipline Center for Voice and Speech voice dosimeter, a device that collects voice data over the course of an entire day, during all activities for 34 hours over 4 days. It was observed that the childs long-term F0 distribution is not normal. If this distribution is consistent in long-term, unstructured natural vocalization patterns of children, statistical mean would not be a valid measure. Author has suggested mode and median as two parameters which convey more accurate information a bout typical F0 physical exertion 65.