Recognition of Marathi Numerals using MFCC and DTW Features


Recognition of Marathi Numerals using MFCC and
DTW Features.

Abstract. Numeral recognition is one among the most vital problems in pattern recognition. Its numerous applications like reading postal zip code, passport number, employee code, bank cheque processing and video gaming etc. To the best of our knowledge, little work has been done in Marathi language as compared with those for other Indian and non-Indian languages. This paper has discussed a novel method for recognition of isolated Marathi numerals. It introduces a Marathi database and isolated numeral recognition system using Mel-Frequency Cepstral Coefficient (MFCC) and Distance Time Warping (DTW) as attributes. The precision of the pre-recorded samples is higher than that of the real-time testing samples. We have also seen that the accuracy of the speaker dependent samples is higher than that of the speaker independent samples. Another method called HMM that statistically models the words is also presented. Experimentally, it is observed that recognition precision is higher for HMM compared with DTW, but the training process in DTW is very simple and fast, as compared to the Hidden Markov Model (HMM). The time required for recognition of numerals using HMM is more as compared to DTW, as it has to go through the many states, iterations and many more mathematical modeling, so DTW is preferred for the real-time applications.

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Keywords: Hidden Markov Model (HMM), Mel-Frequency Cepstral Coefficient (MFCC), Distance Time Warping (DTW).

Introduction
Speech recognition systems are used in different fields in our daily life. Due to the rapid advancement in this field all over the world we can see many systems and devices with voice input 3. Speech Synthesis and Speech Recognition combinely form a speech interface. A speech synthesizer transforms text into speech, so it can read out the textual contents from the screen. Speech recognizer had the ability to find the spoken words and transform it into text. We require such software’s to be available for Indian languages.

Speech recognition in computer domain involves many steps with issues attached with them. The steps needed to make computers perform speech recognition are: Voice recording, word boundary detection, feature extraction, and recognition by using knowledge models 1, 2.

Problem Definition
The aim of the paper is to build a speech recognition tool for Marathi language, which is an isolated word speech recognition devices that uses Mel-Frequency Cepstral Coefficient (MFCC) for Feature Extraction and Distance Time Warping (DTW) for Feature Matching or to compare the test patterns.

3. Marathi Numeral Recognition using MFCC and DTW Features
The popularly used cepstrum based techniques to check the pattern to find their similarity are the MFCC and DTW. The MATLAB is utilized for the implementation of MFCC and DTW attributes.

FEATURE EXTRACTION (MFCC)
The MFCCs are used for feature extraction. The efficiency of this phase is important for the next phase since it affects its behavior 4. In MFCC feature extraction, the magnitude spectrum of windowed speech frame was filtered by using a triangular Mel filter bank have twenty Mel filters. From a group of twenty Mel-scaled log filter bank outputs, MFCC feature vector that consists of thirteen MFCC and the corresponding delta and acceleration coefficients (total thirty nine coefficients) is extracted from every frame.

The widespread use of the MFCCs is because of its low computational complexity and higher performance for ASR in the clean matched conditions. Performance of MFCC degrades drastically in presence of noise and degradation is directly proportional to signal-to noise ratio (SNR). The recognition accuracy for MFCC attribute is taken into account because it mimics the human ear perception 4.

The complete procedure of the MFCC is shown in Fig. 3.1. As shown in Fig. 3.1, MFCC consists of seven computational steps. Every step has its function and approaches as mentioned in brief as follows.

3977005102235Windowing
00Windowing
236220092710Framing
00Framing
108521559055Pre-emphasis
00Pre-emphasis
56134059055Voice input
00Voice input

30778458191500175260099060006654809080500
43795954064000

39674808255DFT
00DFT

97155094615Delta energy ;
Spectrum
00Delta energy ;
Spectrum
429514012382500
235267545720DCT
00DCT
1769110136525Mel
Spectrum
00Mel
Spectrum
281813024130Magnitude spectrum
00Magnitude spectrum
384873576835Mel filter bank
00Mel filter bank

634365323850042862512700 Output
00 Output
17691102984500297942085090Mel Spectrum
00Mel Spectrum
28765506985000
Fig. 3.1. MFCC Block Diagram 4.

Step 1: Pre–emphasis
This method can increase the energy of signal at higher frequency. It permits the passing of every speech signal through a 1st order FIR filter which emphasizes higher frequencies. The 1st order FIR filter equation utilized is
Yn = xn-0.95 x n-1 …………………………………………………………..… (1)
Step 2: Framing.

Every speech signal is split into frames of thirty six ms (milliseconds) and most of spectral characteristics stay the constant in this period, with 50 % of overlapping.
Step 3: Windowing
To eliminate edge effects, every frame is formed with hamming window that
works better than other windows. The hamming window is represented by
Wn=0.54-0.46cos2?nN-1 Where, 0?n?N-1……..….. (2)
Step 4: Fast Fourier Transformation (FFT)
The FFT is employed to get log magnitude spectrum to estimate MFCC. We have utilized 1024 point to obtain higher frequency resolution.
Step 5: Mel Filter Bank Processing
The twenty Mel triangular filters are designed with 50% overlapping. From every filter the spectrum are included to obtain one coefficient each, hence we have considered the first thirteen coefficients as our attributes. These frequencies are converted to Mel scale utilizing conversion formula.

FMel=2595*log101+f700………………………….…… (3)
We have taken into account only 13 MFCC coefficients due to the fact it
gives higher recognition accuracy than other coefficients.

Step 6: Discrete Cosine Transformation (DCT)
The DCT of every Mel frequency Ceptral are used for de-correlation and energy compaction is called as MFCC. The group of coefficient are called MFCC Acoustic Vectors. So, every input speech signal is converted into a sequence of MFCC Acoustic Vector from which reference templates are obtained.
Step 7: Delta Energy and Delta Spectrum
The attributes associated to the variation in cepstral features over time are represented by thirteen delta features (12 cepstral features and one energy feature), and 13 double delta or acceleration attributes. Each of the 13 delta features gives the variation between frames, while each of the 13 double delta attributes gives the variation between frames in the corresponding delta features. In similar way, all the total 39 MFCC feature are estimated for each frame which has feature vector. The Mel filter bank created is shown in Fig.3.2.

4096385766445Frequency
00Frequency
4069715986155Energy in each band
00Energy in each band

Fig. 3.2: Mel scale filter bank 2.

The operating process of the MFCC coefficient extraction is:
Pre-emphasis of the speech signal, frame, adding window, then use the
FFT to get the frequency information.

2. Pass the signal through the Mel frequency coordinate triangle filter sets to
match the human hearing techniques and the human hearing sensibility to
variant speech spectrum.

Estimate the logarithm value of the signal after the Mel filters to get the
logarithmic spectrum.

4. Obtain the discrete cosine transform to the signal and get the MFCC
coefficients.

Mel-frequency wrapping
According to psychophysical studies, human perception of the frequency content of sounds follows a subjectively defined nonlinear scale called the Mel scale .The
speech signal consists of tones with different frequencies F or each tone with an actual frequency measured in Hz, a subjective pitch is measured on the ‘Mel’ scale. The mel-frequency scale is a linear spacing below 1000Hz and above 1000Hz is a logarithmic spacing 3.

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