Machine Learning Techniques for Speech Recognition using the Magnitude

According to the high performance that Convolutional Neural Networks (CNN) have had in pattern recognition it has been begun to apply the concepts of convolution in DNN to speech recognition obtaining hybrids between convolution and fully connected layers [6]. However, at first, they did not exceed 3 convolution layers combined with a higher number of fully connected layers which limits the ability to acquire patterns and makes the performance not really high [7]. Recently, architectures began to be developed deeper and in combination with other types of neural networks like the recurrent neural networks which help in the temporal relationship that may exist between signal divisions, improving the reduction of error in phonetic recognition [8].

The architectures that have been developed, according to the state of the art have as input feature maps of phonemes which are used in large vocabulary continuous speech recognition, however for more basic applications, the use of phonemes makes them more complicated for its implementation, so, an alternative to reduce the complexity is the recognition of certain number of words delimited by the application, making the implementation of a CNN more feasible and achieve a better performance by the parameters that have to learn [9]. For this reason, in this work two CNN architectures based on the recognition of complete words are built which have not been developed in the state of the art as a complement to this research area [10].

In simple words speech recognition could be defined as the development of converting speech signal to a progression of words by means of algorithm demonstrated as a computer program [11]. Speech processing is the main areas of signal processing. The contribution of speech recognition is to develop a method for speech input to machine based readable script, which can be very well used in libraries, banks, and various workplaces for effective management [12]. Regular speech recognition today discovers widespread relevance in tasks that necessitate human machine interface like automatic call processing [13]. Fig. 1 illustrates the architectural framework for generating speech recognition.

Fig. 1. Architectural framework.

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Speech technologies must be developed for decades as a distinctive signal processing region, while the preceding decade has brought an enormous development according to the latest machine learning paradigms. In spite of the intrinsic complexity has been proposed that the relations with cognitive systems, speech recognition methods are the most common interdisciplinary knowledge area. The paper fully based on to develop the machine learning methods that are computational intelligence contributes to provide fields that cover speech construction and auditory observation that demonstrates the speech transmission and language, speech recognition. The spoken dialogue construction is consequently speech synthesis.

Moreover, this paper demonstrates the concepts of recent developments in speech signal demonstration, coding that include cognitive coding. The main objective of this paper is to highlight the recent development according to the latest machine learning methods that has an impact in the development of speech signal processing.

The main objective of speech recognition is too capable of understand the spoken information. The related methods did not execute the expected accuracy, so the proposed methodology has been constructed to implement the speech recognition in an automatic manner with speaker identity. This system could be constructed to implement the feature extraction, modeling, analyzing and also testing process.

A suitable feature which can differentiate the “Cholo” and “Thamo” signal is magnitude analysis of the given signals.

A general condition feature can be applied as,

Here is common scenario of a Cholo signal where upper figure is original signal and below the figure is pre-processed signal plot. As I can observe that, here the highest max is 1 and if I take another two max before the highest and after the highest those max magnitudes are very near to the highest and differences are in between 0.3 range. Now, the general scenario of the Thamo signal plot which is given besides, after the pre-processing, the maxs of before and after highest of Thamo signals are quite far (in magnitude) at all.

Therefore, we have set a condition of if-else in my MATLAB code as, If before max and after max is less than [highest-0.3], that signal is Thamo signal. Else of the condition is, Thamo signal; where, before max and after max is greater than [highest-0.3], that means in between this range

The development of speech recognition methods is constructed using the innovative machine learning methodologies to implement the accuracy level. According to the improvement of the machine learning approach has been utilized to provide the excessive impact to this development. HCI systems are having the spoken language understanding and demonstration.

The machine learning concept has automation about the speech recognition system to translate the text to speech technology. It is demonstrated that the system with deep learning approach could be capable of recognizing the speech components in an acoustic surrounding with the capability of analyzing the accuracy with human based interaction system using HCI of style and speaker quality.

The reinforcement related machine learning methodology has been utilized the unsupervised methodology to develop the progress with huge number of datasets. The speech signal is involved to provide the automatic speech recognition in accurate way and signal based synthesis detection to generate the adaptive quantization in view of speech coding approaches.

The innovative methods could be constructed to provide the robustness in a huge amount of dynamic speech signal demonstrations. The frame adaptive approach can indicate that the machine learning tools to increase the popularity and provided the exact solutions. The improved performances of the proposed system have proved by utilizing the predictive coefficients. An increasing amount of progress in speech signal processing with machine learning approach.

Neural networks have incorporated the Markov chain methodology that the statistical methods are demonstrated as a graph. The Markov models have be utilized for providing the probability based state transitions that the neural networks utilize the connection based state transition. The common dissimilarity within the neural network and Markov model is the parallel connectivity. The state transition is the common strength of the connectivity. Artificial Neurons are the common component of Artificial Neural Network that utilizes the function of organic neuron. It is a mathematical formation that is conceived as a representation of natural neuron. Fig. 3 demonstrates the common artificial neuron.

Fig. 3. Structure of Artificial Neural Network.

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Types of Artificial Neural Network

A Feedforward Network is a mathematical model according to the construction of biological neural networks. The demonstration that flows within the network influences the architecture of the ANN since a neural network modification based on those functionalities. ANNs are measured as non-linear statistical information modeling components that are the multifaceted relationships within the inputs and outputs are modeled. ANN is also called as a neural network. ANNs have 3 layers that are interconnected with other related systems. The initial layer contains the input neurons. Those neurons forward the information to the middle layer, which in turn forwards the output neurons to the final layer. Fig. 4 illustrates the demonstration of the Feedforward network layer.

Fig. 4. Feedforward network.

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Artificial neural networks are an effort to obtain computers to effort more like the human brain. Your brain doesn’t accumulate detailed encoded functions; it has enormous networks of neurons. These change their communications to every other as latest data past throughout them. Speech recognition with this machine learning is completed with big data-based organizations of data to train these networks.

An ANN has a lot of positives but one of the mainly recognized of these is the reality that it preserves essentially train from scrutinizing the data sets. In this approach, ANN is utilized like a random function estimate component. These types of components assist approximation the nearly all cost-effective and ideal approaches for finding the answers while defining computing distributions. ANN understands data samples rather than whole information to demonstrate at solutions, which has the time utilization. ANNs are measured reasonably simple mathematical elements to augment existing data analysis approaches.

Speech Recognition utilizes the implementation of audio files with AudioFile class. This class could be started with the way to an audio file and presents a context manager edge.

3.6.1. Supported File Types

Presently, Speech Recognition wires the following file formats:

• WAV: must be in PCM/LPCM format

• AIFF

• AIFF-C

• FLAC: must be native FLAC format; OGG-FLAC is not supported

Recognizing speech needs audio input, and Speech Recognition uses retrieving this input. The Speech Recognition will have you up and operation in just a small number of minutes and it is demonstrated in Fig. 5. The suppleness and ease-of-use of the Speech Recognition package create it as a replacement for any Python coding. Moreover, maintain for every characteristic of every API it wraps is not definite. You will require expending more time for identifying the obtainable options to find out if Speech Recognition may utilize in your scrupulous case and it is illustrated in Fig. 6 and Fig. 7.

Fig. 5. Retrieving input from audio input.

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Fig. 6. API formation.

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Fig. 7. Output values.

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ANNs are comparatively rudimentary electronic networks of “neurons” according to the neural organization of the brain. They procedure has been constructed to store data, analyzing their classification of the system with the mostly used classification of the system. The errors from the original classification of the initial record is fed back into the network, and utilized to adapt the networks algorithm the next time.

In the training phase, the correct class for each record is known (this is termed supervised training), and the output nodes can therefore be assigned “correct” values -- “1” for the node corresponding to the correct class, and “0” for the others and it is illustrated in Fig. 8.

Fig. 8. Training of ANN.

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clc

clear all

close all

signals = [‘E:\nufaer\test_data\V-2.mp3’];

audo = audioread(signals);

figure;

subplot(2,1,1);

plot(audo);

title(‘Original audio signal’);

m=400;

b=(1/m)*ones(1,m);

pstv=abs(audo);

audof=filter(b,1,pstv);

audof=audof / max(audof);

subplot(2,1,2);

plot(audof);

title(‘Pre-processed signal’);

maxmag=max(audof)

maxloc=find(audof==maxmag);

nextmag=max(audof(maxloc+5000:end))

prevmag=max(audof(1:maxloc-5000))

if((nextmag<(maxmag-0.3))&&(prevmag<(maxmag-0.3)))

disp(‘signal is thamo’);

else

disp(‘signal is cholo’);

end