**Acknowledgements**

This work was supported by Canadian Institutes of Health Research #389769 and Natural Sciences and Engineering Research Council (Canada) RGPIN-2018- 05450. All figures were created with Biorender.com.


2. Mel frequency cepstral coefficients (MFCC)

naturally acquired human motor abilities, a task categorized in regular adults by the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles connected by spinal and cranial nerves. The simplicity with which human beings speak is in contrast to the complexity of the task, and that complexity could assist in explaining why speech

There have been several successful attempts in the development of systems that can analyze, classify and recognize speech signals. Both hardware and software that have been developed for such tasks have been applied in various fields such as health care, government sectors and agriculture. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards [4]. Speaker recognition executes a task similar to what the human brain undertakes. This starts from speech which is an input to the speaker recognition system. Generally, speaker recognition process takes place in three main steps which are acoustic processing, feature extraction

The speech signal has to be processed to remove noise before the extraction of the important attributes in the speech [6] and identification. The purpose of feature extraction is to illustrate a speech signal by a predetermined number of components of the signal. This is because all the information in the acoustic signal is too cumbersome to deal with, and some of the information

Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively lesser data rate for subsequent processing and analysis. This is usually called the front end signal-processing [9, 10]. It transforms the processed speech signal to a concise but logical representation that is more discriminative and reliable than the actual signal. With front end being the initial element in the sequence, the quality of the subsequent features (pattern matching and speaker modeling) is significantly affected by the quality of the

Therefore, acceptable classification is derived from excellent and quality features. In present automatic speaker recognition (ASR) systems, the procedure for feature extraction has normally been to discover a representation that is comparatively reliable for several conditions of the same speech signal, even with alterations in the environmental conditions or speaker, while retaining the portion that characterizes the information in the speech signal [7, 8].

Feature extraction approaches usually yield a multidimensional feature vector for every speech signal [11]. A wide range of options are available to parametrically represent the speech signal for the recognition process, such as perceptual linear prediction (PLP), linear prediction coding (LPC) and mel-frequency cepstrum coefficients (MFCC). MFCC is the best known and very popular [9, 12]. Feature extraction is the most relevant portion of speaker recognition. Features of speech have a vital part in the segregation of a speaker from others [13]. Feature extraction reduces the magnitude of the speech signal devoid of causing any damage to the

Before the features are extracted, there are sequences of preprocessing phases that are first carried out. The preprocessing step is pre-emphasis. This is achieved by passing the signal

can be very sensitive to diseases associated with the nervous system [3].

4 From Natural to Artificial Intelligence - Algorithms and Applications

and classification/recognition [5].

front end [10].

power of speech signal [14].

is irrelevant in the identification task [7, 8].

Mel frequency cepstral coefficients (MFCC) was originally suggested for identifying monosyllabic words in continuously spoken sentences but not for speaker identification. MFCC computation is a replication of the human hearing system intending to artificially implement the ear's working principle with the assumption that the human ear is a reliable speaker recognizer [19]. MFCC features are rooted in the recognized discrepancy of the human ear's critical bandwidths with frequency filters spaced linearly at low frequencies and logarithmically at high frequencies have been used to retain the phonetically vital properties of the speech signal. Speech signals commonly contain tones of varying frequencies, each tone with an actual frequency, f (Hz) and the subjective pitch is computed on the Mel scale. The mel-frequency scale has linear frequency spacing below 1000 Hz and logarithmic spacing above 1000 Hz. Pitch of 1 kHz tone and 40 dB above the perceptual audible threshold is defined as 1000 mels, and used as reference point [20].

MFCC is based on signal disintegration with the help of a filter bank. The MFCC gives a discrete cosine transform (DCT) of a real logarithm of the short-term energy displayed on the Mel frequency scale [21]. MFCC is used to identify airline reservation, numbers spoken into a telephone and voice recognition system for security purpose. Some modifications have been proposed to the basic MFCC algorithm for better robustness, such as by lifting the log-melamplitudes to an appropriate power (around 2 or 3) before applying the DCT and reducing the impact of the low-energy parts [4].


**8. Conclusion**

This chapter reviewed the relationship between the bio-polymers and the modeling of the production processes in textile industry. The theoretical techniques and factorial experimental design together with the biopolymers' classification and preparation methods open new field of the modern textiles, from fiber to fabric forming and coating technologies. Biopolymer use in the textile industry is an exciting and innovative area of research for scientists and researchers alongside textile and polymer engineers.

In the case study, results obtained should answer the fairly complex demands posed by multi-applications running concurrently with the application programs (or processes) in the computer. It is limited by the regions of the studied factors between the factor levels. The program's results help in achieving a balance between the enhanced properties and the fiber cost while saving processing cost, material and the power required for enhancing fibers. After finishing the processes for modeled biodegradable fibers, the process conditions (process-input data) selected depends upon the user. The produced environmentally friendly, economical, energy saving fibers can be potentially utilized in textiles, agricultural, as well as horticultural applications.

*Characterization, Modeling and the Production Processes of Biopolymers in the Textiles Industry DOI: http://dx.doi.org/10.5772/intechopen.96864*


**4. Training algorithms for recurrent neural networks**

A training algorithm is a procedure that adapts the free parameters of a neural network in response to the behavior of the network embedded in its environment. The goal of the adaptations is to improve the neural network performance for the given task. Most training algorithms for neural networks adapt the network parameters in such a way that a certain error measure (also called cost function) is minimized. Alternatively, the *negative* error measure or a *performance measure* can be maximized.

Error measures can be postulated because they seem intuitively right for the task, because they lead to good performance of the neural network for the task, or because they lead to a convenient training algorithm. An example is the Sum Squared Error measure, which always has been a default choice of error measure in neural network research. Error measures can also be obtained by accepting some general.

Different error measures lead to different algorithms to minimize these error measures. Training algorithms can be classified into categories depending on certain distinguishing properties of those algorithms. Four main categories of algorithms that can be distinguished are:( Dijk, 1999)

The following algorithms for the Fully Recurrent Neural Network and the subsets of the FRNN are investigated in this item: