Speech To Text Conversion Using Natural Language Processing

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1 Speech To Text Conversion Using Natural Language Processing S. Selva Nidhyananthan Associate Professor, S. Amala Ilackiya UG Scholar, F.Helen Kani Priya UG Scholar, Abstract Speech is the most effective form of communication between human and its environment. Speech has potential of being important mode of interaction with computer. This paper deals with speech recognition. The process of converting speech into written language requires special technique as it must be fast and should be correct to be understandable. The automatic continuous speech recognition (ASR) system maps the acoustic signal to a string of words. Speech recognition system involves various process like feature extraction, acoustic modeling, language modeling and viterbi decoder which produces string of words as output. measurements for each second of speech, so it is important to store amplitude measurements efficiently. They are usually stored as integers, either or 16 bit. This process of representing real-valued numbers as integers is called quantization. Keywords Speech Recognition, Feature Extraction, Gaussian Acoustic Model, N-gram Language Model, Viterbi Decoder. I. Introduction Automatic Speech Recognition (ASR) can be described as self-mechanized way of speech transformation which converts the audio signal form into textual form. It involves classifying the spoken words and displaying the result in text form. The various challenges in this area are accuracy, noise removal and information retrieval. Various steps are involved in the process of converting speech to text. The two main stages in speech recognition system are feature extraction and classification [2]. In feature extraction stage, characteristic of input speech signal is extracted and in classification stage the uttered word is recognized.. Fig. 1: Voiced speech segment and its spectrum. A. Characteristics of Speech Speech sounds can be broken into three classes which depends on their mode of excitation- Voiced sound, unvoiced sound and Plosive sound. Fig. 1 depicts voiced speech segment and its spectrum with formant peaks. It exclaims the quasi-periodic pulse like speech waveform. Fig. 2 depicts about unvoiced speech segment and its spectrum. It results in a noise like waveform. Most information in human speech is in frequencies below 10,000Hz, so a 20,000Hz sampling rate would be necessary for complete accuracy. But telephone speech is filtered by the switching network, and only frequencies less than 4,000Hz are transmitted by telephones. Thus, an 8,000Hz sampling rate is sufficient for telephonebandwidth speech like the switchboard corpus. A 16,000Hz sampling rate is often used for microphone speech. Even an 8,000Hz sampling rate requires 8,000 amplitude Fig. 2: Unvoiced speech segment and its spectrum. B. Proposed Methodology Automatic speech understanding retains only the important contents from what the speaker spoke in the text format. Fig. 2a shows the proposed method block diagram. Speech Recognition process is a special case of Bayesian inference. The goal is to combine various probabilistic models to get a complete estimate for the probability of a noisy acoustic observation-sequence given a candidate source sentence. Since the search space is so large in speech ISSN : Page 77

2 recognition, efficient algorithm has to be used to find the uttered sentence. II. Feature Extraction Feature extraction [1] is the most important part of the entire speech recognition system. The aim of feature extraction is to reduce the data size of the speech signal before pattern classification or recognition. The steps for Mel frequency Cepstral Coefficients (MFCCs) calculation are framing, windowing, Discrete Fourier Transform (DFT), Mel frequency filtering, logarithmic function, Discrete Cosine Transform (DCT), Deltas and Energy. Fig. 3 shows the block diagram of MFCC feature extraction. A. Pre-emphasis The first step in MFCC feature extraction is to boost the amount of energy in the high frequencies. If we look at the spectrum for voiced segments like vowels, there is more energy at the lower frequencies than at higher frequencies. This drop in energy across frequencies is due to the nature of the glottal pulse. Boosting the high frequency energy makes information from these higher formants available to the acoustic model and improves phone detection accuracy [13]. This pre-emphasis is done with a filter. Fig. 4a shows the input audio signal and Fig. 4b shows the pre-emphasized signal. Fig.4 MFCC block B. Windowing Technically, speech is a non-stationary signal, meaning that its statistical properties are not constant over time. We extract the roughly stationary portion of speech by using a window which is non-zero inside a certain region and zero elsewhere, running this window across the speech signal and extracting the waveform inside this window. The windowing process is characterized by three parameters: the width of the window, the offset between successive windows, and the shape of the window. To extract the signal we multiplied the value of the signal at time n, s[n] by the value of the window at time n, w[n]: n sn y w n. (1) More common window in MFCC extraction is the hamming window [14], which shrinks the values of the signal towards zero at window boundaries, avoiding discontinuities. C. Discrete Fourier Transform The tool used for extracting spectral information for discrete frequency bands for a discrete-time signal is the discrete Fourier transform or DFT. X k N 1 n0 j2kn ne N x. (2) The input to the DFT is the windowed signal x[n] x[m], and the output, for each of N discrete frequency bands of the windowed signal, is a complex number X[k] representing the magnitude and phase of that frequency component in the original signal. A commonly used algorithm for computing the DFT is the fast Fourier transform or FFT. This implementation of the DFT is very efficient. D. Mel Filter Bank and Log The result of the FFT is the information about the amount of energy at each frequency band. Human hearing, however is not equally sensitive at all frequency bands. It is less sensitive at higher frequencies, roughly above 1,000Hz. Modeling this property of human hearing during feature extraction improves speech recognition performance. The form of the model used in MFCCs [12] is to wrap the frequencies output by the DFT onto the mel scale. The relationship between frequency in hertz and the mel scale is linear below 1000Hz and logarithmic above 1000Hz. The mel frequency m can be computed from the raw acoustic frequency as follows: f mel f 1127 ln 1. (3) 700 This implementation is done by using bank of filters that collect energy from each frequency band, with 10 filters spaced linearly below 1000Hz and the remaining filters spread logarithmically above 1000Hz. Finally, we take the log of each of the mel spectrum values. E. The Cepstrum: Inverse Discrete Fourier Transform Next in MFCC feature extraction is the computation of the cepstrum. The cepstrum [15] has a number of useful processing advantages and also significantly improves phone recognition performance. It also separates the source and filter. Cepstrum is the spectrum of the log of the spectrum. The cestrum is more formally defined as the inverse DFT of the log magnitude of the DFT of a signal; hence, for a windowed frame of speech x[n], ISSN : Page 78

3 c N 1 2kn 2kn j N j n xn N log e e n0 N 1. (4) n0 For a single frame we took 12 cepstral values. These 12 coefficients represent the information solely above the vocal tract filter, cleanly separated from information about the glottal source. F. Deltas and Energy Along with the 12 cepstral coefficients for each frame 13 th feature: the energy from the frame is added. Energy correlates with phone identity and so is a useful cue for phone detection. The energy in a frame is the sum over time of the power of the samples; thus, for a signal x in a window from time sample t 1 to time sample t 2, the energy is t Energy 2 2 x t. (5) t t1 Then a delta or velocity feature and double delta or acceleration feature [1] is computed for each of the 13 features. The delta value d (t) for a particular cepstral value c(t) at time t can be estimated as t c t 1 ct 1 d.. (6) III. Acoustic modeling 2 An acoustic model [10] is created by taking audio recordings of speech, and its text transcriptions, and to create statistical representations of the sounds that makeup each word. It is used by a speech recognition engine to recognize speech. The acoustic model breaks the words into the phonemes. A. HMM In HMM the hidden states are parts-of-speech and the observations of words. The HMM decoding task maps the sequence of words to sequence of parts-of-speech. The observation sequence for speech recognition is a sequence of acoustic feature vectors. Observations are drawn for every 10 milliseconds, so 1 second of speech requires 100 spectral feature vectors, each vector of length 39. The phonemes in speech follow the left to right sequences, so the structure of HMM is a left-to-right structure. The states of HMM model represent the word or acoustic phonemes in speech recognition. The number of states in HMM model is randomly chosen to model. The choice of the number of states causes to change the feature vectors or observations. It affects the recognition rate or accuracy of speech recognition. The most flexible approach for speech recognition is Hidden Markov Models (HMMs). HMM [6] is the popular statistical tool for modeling a wide range of time series data. HMM word model λ is composed of initial state probability (π), state transition C. HMM algorithms are Likelihood computation (Forward algorithm), Most probable state sequence (Viterbi algorithm) and Estimating the parameters (EM algorithm). IV. Language Modeling Language modeling [8] is used in many natural language processing applications. It tries to capture the properties of a language and to predict the next word in the speech sequence. The language model compares the phonemes to words in its built in dictionary. The probabilities of the words are computed using the n-gram language model. Simplest model of word probability: 1/T. N-gram probabilities come from a training corpus. A separate test corpus is used to evaluate the model. V. Viterbi Decoder Viterbi algorithm [5] is used for finding optimal sequence of hidden states. Given an observation sequence of words and an HMM, the algorithm returns the state path through the HMM that assigns maximum likelihood to the observation sequence. Viterbi decoding algorithm is usually implemented with pruning and then called beam search. The algorithm takes as input a sequence of cepstral feature vectors, a GMM acoustic model, and an N-gram language model and produces a string of words. VI. Results & Discussion The input to the speech recognition block is audio or speech (wav file). The input signal is first sampled and then quantized. The signal is framed to make it a stationary signal over that particular frame duration. The shift duration of the frame is selected such that they are overlapped. This is done in order to avoid any discontinuities in the signal. Fig. 5a shows the input signal ISSN : Page 79

4 Fig. 5b Pre-emphasized signal The signal is pre-emphasized in order to remove the spectral tilt and it boosts the amount of energy in the higher frequencies. Fig. 6 shows the MFCC coefficients for the first frame. N-gram language model is used to predict the N th word from the previous N-1 words. Tokenizing is done to split the input sentence into words (Unigram, Bigram, etc.,) Fig.9 Unigram Tokenization The probabilities of the word are calculated to find the most probable word. Log likelihood values are also estimated during acoustic modeling. The outputs from acoustic model and language model are given as the input to the viterbi decoder to recognize the uttered sentence. Fig. 6 MFCC coefficients These coefficients are obtained by transforming the input waveform into a sequence of acoustic feature vectors, each vector representing the information in a small time window of the signal. Fig. 7 shows the ΔMFCC coefficients for the first frame. VII. Conclusion The accuracy of the speech recognition system depends upon the pronunciatoin of the speaker and the noise in the input speech signal. For the signal with noise, noise removal has to be done in order to enhance the speech recognition. Fig. 7 ΔMFCC coefficients ΔMFCC coefficients are the first derivative of the MFCC vectors. The second derivative is also obtained to the accuracy of recognition. Fig.8 shows the ΔΔMFCC coefficients. References [1] Daniel Jurafsky and James H.Martin, Speech and language processing, Pearson Education Limited, [2] Nuzhat Atiqua Nafis and Md.Safaet Hossain, Speech to Text Conversion in Real-time, International Journal of Innovation and Scientific ResesarchISSN Vol.17 No.2 Aug 2015,pp [3] Sadaoki Furusi, Tomonori Kikuchi, Yousuke Shinnaka, and Chiori Hori,Speech-to-Text and Speech-to-Speech Summarization of Spontaneous Speech, IEEE Transactions on Speech and Audio Processing, Vol. 12, NO. 4, July [4] Arkadiy Prodeus and Kateryna Kulcharicheva, Training of Automatic Speech Recognition System on Noised Speech, th International Conference on /methods and Systems of Navigation and Motion Control (MSNMC) Proceedings pp [5] Y Rajeev Kumar, A Venkatesh Babu, K A Naveen Kumar, John Sahaya Rani Alex, Modified Viterbi Decoder for Hmm Based Speech Recognition System, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). [6] Mark Gales and Steve Young, The Application of Hidden Markov Models in Speech Recognition, Foundations and Trends in Signal Processing Vol.1, No.3 (2007) Fig. 8 ΔΔMFCC coefficients. ISSN : Page 80

5 [7] Arshpreet Kaur, Amitoj Singh, Optimizing Feature Extraction Techniques Constituting Phone Based Modelling onconnected Words for Punjabi Automatic Speech Recognition Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept , 2016, [8] B.H. Juang& Lawrence R. Rabiner, Automatic Speech Recognition -A Brief History of the Technology Development. [9] Bhiksha Raj, Rita Singh, Design and Implementation of Speech Recognition Systems, Spring [10] A. Bharath and Sriganesh Madhvanath, HMM-Based Lexicon- Driven and Lexicon-Free Word Recognition for Online Handwritten Indic Scripts, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 34, No. 4, April [11] Douglas O Shaughnessy, Acoustic Analysis for Automatic Speech Recognition, Proceedings of the IEEE Vol. 101, No. 5, May [12] Kiran Kumar Bhuvanagiri, Sunil Kumar Kopparapu, Modified Mel Filter Bank to Compute MFCC of Subsampled Speech. [13] Radek Safarik and Lukas Mateju, Impact of Phonetic Annotation Precision on Automatic Speech Recognition Systems, IEEE [14] Shikha Gupta, Jafreezal Jaafar, Wan Fatimah wan Ahmad and Arpit Bansal Feature Extraction Using MFCC, Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August [15] Parwinder Pal Singh, Pushpa Rani, An Approach to Extract Feature using MFCC, IOSR Journal of Engineering (IOSRJEN) Vol. 04, Issue 08, August ISSN : Page 81

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