Problem 3: LearningĪdjusting the model parameters to maximize Probability so as to describe how given observation sequence comes out. Solution for the Decoding problem is also Viterbi algorithm. Using optimal criterion is the best possible solution. There is no correct solution for uncovering hidden part of the problem. Problem 2: Hidden State Determination (Decoding)Ĭhoosing corresponding state sequence is quite optimal. Viterbi Algorithm is used for this Evaluation problem but Forward Algorithm is used as well. We have to choose the one with maximum Probability will give better result. Three basic problems for HMM: Problem 1: Evaluation ProblemĬomputing the Probability that observed sequence was produced by model. It matches the Feature vector to the previously trained Phonemes and recognizes the sequence of phonemes and gives output recognized word in text format. Gaussian Mixture Model Classifies the Phonemes to the Feature Vector during Training the Model, whereas during Testing it follows reverse procedure. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm.
GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. It is used as a classifier to compare the features extracted from Feature vector with the stored templates. Gaussian Mixture Model is used mostly for Feature Matching. Phonemes are the distinct units of sound that can distinguish one word from another. It gives the relation between Feature vector and the Phonemes. Thus, it extracts the Feature vector from the given audio signal.įeature Matching is the process of training the acoustic model with the Feature Vector Extracted. Short time Fourier transform divides speech signal into smaller parts and compute Fourier transform of each which gives the Fourier spectrum of each segment of speech signal. It actually calculates the Power of the Spectrum. Each Second is divided into 30 to 50 frames. This Process divides the entire Audio signal into seconds. The widely used method for feature extraction is MFCC. This represents amplitude of speech signal at each spectrum. Discrete cosine transform (DCT) is performed after taking logarithms. Mel Frequency Cepstral Coefficients (MFCC):įourier transform of the given signal are mapped to the Mel scale (nonlinear frequency scale). Mel Frequency Cepstral Coefficients (MFCC).Three methods are used for Feature Extraction. It gives the amplitude of Entire audio signal into vector format which is Digital. Output of Feature Extraction is Feature Vector. Each Frame is converted from Time domain to Frequency domain by using Fourier Transforms. Each part of Speech signal is called Frame. In Feature Extraction, primarily the signal is divided into small periods say 10ms. This process is known as Feature Extraction. Hence the audio signal needs to be converted into digital format. Input is speech or audio signal which is in analog form where system cannot understand analog signal.
Speech recognition, Image Recognition, Gesture Recognition, Handwriting Recognition, Parts of Speech Tagging, Time series analysis are some of the Hidden Markov Model applications.ģ. Hidden Markov Models are widely used in fields where the hidden variables control the observable variables. In Speech Recognition, Hidden States are Phonemes, whereas the observed states are speech or audio signal. It is traditional method to recognize the speech and gives text as output by using Phonemes. Speech Recognition mainly uses Acoustic Model which is HMM model. Hidden Markov Model explains about the probability of the observable state or variable by learning the hidden or unobservable states. In Hidden Markov Model, the state is not visible to the observer (Hidden states), whereas observation states which depends on the hidden states are visible.
The current state always depends on the immediate previous state. Hidden Markov Model is the set of finite states where it learns hidden or unobservable states and gives the probability of observable states. Understanding Hidden Markov Model for Speech Recognition Hidden Markov Model: