Limited Speech Recognition for Controlling Movement of Mobile Robot Implemented on ATmega162 Microcontroller

Growth in Digital signal processing technology gives positives influences in human life. One of the branch of science that gives significant influence is digital speech processing. It can be expand into some applications that make human life easier. Digital speech processing is appropriate to speech recognition. Speech recognition is used to arrange the movement of remote control car. The remote control car will move according to our speech. This final project is closely related with speech recognition. The LPC (Linear Predictive Coding) method will extract the speech signal features and HMM (Hidden Markov Model) to modelling the speech signal are used. It is done by comparing model from extracting feature that is available in HMM modelling. Models will be used in the speech recognition process, if the models have highest level of conformity. The experiment has been done in two conditions, i.e ideal condition in room wih low noise level and unideal condition in room with noise. Result of the experiment from the whole sistem performace at ideal condition is 97,71% for people that have been inputed in the database, and 95,42% for people which have not been inputed in the database. Result of the experiment of database say it regulary is 97,14%. Result of the experiment of database at unideal condition is 54,28% for inputing word at noisy area, result of the experiment of database for inputing word at high frequency from siren voice is 98,57% and result of the experiment of inputing similar word with database is 97,6% Keywords: speech recognition, LPC, HMM

Speech recognition can be defined as the process of converting voice signals into the ranks of the word, by applying a specific algorithm that is implemented in a computer program. The research of speech recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for speech recognition in Indonesian language. This is important because the method can produce a high accuracy for a particular language does not necessarily produce the same accuracy for other languages, considering every language has different characteristics. Thus this research hopefully can help further accelerate the use of automatic speech recognition for Indonesian language. There are two main processes in speech recognition, feature extraction and recognition. The method used for comparison feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden Markov Model (HMM). The test results showed that the LPC method is better than MFCC in Indonesian language speech recognition.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Speech signal analysis aims to identify patterns within data to develop effec tive recognition algorithms. This process primarily utilizes feature extraction techniques such as linear predictive coding (LPC), linear predictive cepstral co efficients (LPCCs), and Mel-frequency cepstral coefficients (MFCCs). These features are crucial for constructing recognition algorithms that leverage both statistical and deep learning methods. While deep learning models require ex tensive datasets, they often prove unsuitable for low-resource languages. The Hidden Markov model (HMM)is the most widely adopted statistical framework in speech processing. However, HMMs are characterized by state-dependent models, where each state interacts only with its neighboring states. This limita tion restricts HMMs from capturing long-term signal properties, highlighting the need for addressing these constraints at the feature extraction stage. Most feature extraction methods rely on short-term signal processing, which further limits the comprehension of speech utterances. To overcome these limitations, alter native methods are necessary to capture more comprehensive patterns. This pa per presents a graph-based approach for analyzing speech trajectories and their temporal properties, which are subsequently validated using HMMs in speech recognition tasks. Graph-based representations on a low-resource Telugu dataset improve recognition accuracy by 13% while reducing processing time compared to traditional LPC.

A hidden Markov model based classifier is proposed in this paper to perform automatic speech recognition using myoelectric signals from the muscles of vocal articulation. The classifier's resilience to temporal variance is compared to a linear discriminant analysis classifier that was used in a pervious study. Speech recognition was performed, using five channels of myoelectric signals, on isolated words from a 10-word vocabulary. Temporal variance was induced by temporally misaligning data from the test set, with respect to the training set. When compared to the LDA classifier, the hidden Markov model classifier demonstrated a markedly lower variation in classification error due to the temporal misalignment. Characteristics of the hidden Markov model MES classifier suggest that it would effectively complement a conventional acoustic speech recognizer, in a multi-modal speech recognition system.

Much of the progress in automatic speech recognition is attributable to the use of hidden Markov models (HMMs) to characterize acoustic speech patterns. Despite their success, HMMs make little use of knowledge about the speech signal, and variation that may be explicable in terms of the physical properties of the human speech production system is treated as random. There is, therefore, a need to develop a speech modeling paradigm which reflects human speech processes more closely. Segmental hidden Markov models (HMMs) are extended versions of conventional HMMs in which states are associated with sequences of observation vectors rather than individual vectors. By treating a segment as a homogeneous unit, dependencies between vectors within a segment can be modeled explicitly. This letter describes a segmental HMM in which a segment is modeled as a noisy function of a linear trajectory. The basic theory of the model is presented, together with formulae for model parameter optimization.

Over the past few decades, designers are considering a range of applications ranging from mobile communications to automatic machine learning. Speeches are less commonly used in the electronic and computer field due to the complexity and variety of signals and sounds. By the use of modern algorithms and methods, speech signals are processed to recognize text. In this project, we will build a online speech to text engine. The program receives speech during the run through the microphone and uses sample speech to recognize the text. The known text can be saved to a file. This is being developed on Java platform using the eclipse workbench. Our speech-to-text program directly gets and converts speech into text. It can add other great applications, giving users a different choice of data input. A text-to-speech system can also improve accessibility of the system by providing data access options for users who are blind, deaf or disabled. Voice SMS application allows the user to record and convert spoken messages into an text message. The user can send messages to the phone number which is entered. Speech recognition is done through the Internet, connecting to Google's server. The application is based on input messages in English. Speech recognition uses a technique based on hidden Markov models (HMM - Hidden Markov Model). It is currently the most effective and flexible method of speech recognition.

Both linear predictive coding (LPC) and mel scale frequency cepstral coefficient (MFCC) analysis, the most common techniques for speech recognition signal processing, make the assumption that the speech signal is stationary for some analysis window and produce a representation based upon the "stationary" frequency content within the window. This work uses a technique based upon Cohen's (1989) class of generalized time frequency representations (TFR) to produce selected frequency representations that are not based upon an assumption of stationarity. This representation is used in a speech recognition system to produce improved accuracy. The proposed approach requires a kernel design to specify the attributes of the representations. The considerations used for analyzing speech signals and the resulting attributes are discussed. Comparisons with standard analysis techniques are presented. The significant computational requirements are also discussed.

Great strides have been made in the development of automatic speech recognition (ASR) technology over the past thirty years. Most of this effort has been centered around the extension and improvement of Hidden Markov Model (HMM) approaches to ASR. Current commercially-available and industry systems based on HMMs can perform well for certain situational tasks that restrict variability such as phone dialing or limited voice commands. However, the holy grail of ASR systems is performance comparable to humans—in other words, the ability to automatically transcribe unrestricted conversational speech spoken by an infinite number of speakers under varying acoustic environments. This goal is far from being reached. Key to the success of ASR is effective modeling of variability in the speech signal. This tutorial will review the basics of ASR and the various ways in which our current knowledge of speech production, speech perception and prosody can be exploited to improve robustness at every level of the system.

In recent years, with the rapid development of Deep Learning (DL) and the widespread uses of Deep Neural Networks (DNN), speech recognition technology has attracted great attention. This technology enables machines to convert human speech into text and plays an important role in some areas such as virtual assistants, conference transcription and smart home devices. Traditional models such as Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) used to play a key role in modeling and recognizing speech signals. Even though these methods still have challenges in dealing with high‐dimensional feature vectors as they struggle to efficiently learn large amounts of model parameters while maintaining good generalization performance. The emergence of DNNs has revolutionized the field of speech recognition, making it possible to automatically extract features from raw speech signals. Thereby, modern speech recognition systems can exhibit higher adaptability and generalization capabilities, achieving higher accuracy and stability under various conditions. This review explores the application of DL techniques to speech recognition, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformer‐based architectures, etc. Advances in these techniques have dramatically improved recognition accuracy, noise immunity, and adaptability to multiple accents, thereby improving the total efficiency of automatic speech systems. By leveraging advanced feature extraction and modeling capabilities, deep learning‐based systems support real‐ time processing and minimize the dependency on traditional manually designed features. However, there are still limitations in terms of interpretability, computational efficiency, and fairness to different accents and languages, which impede the widespread use of these techniques. Future research should focus on solving these issues in order to achieve fairer, efficient, and scalable deep learning‐driven speech recognition schemes.

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations.

Arabic language can be used by native and non- native speakers; due to Arabic is the language of the holy book of Muslims. In this paper, Arabic phoneme recognition system is proposed based on Malay speakers. This system consists of three main stages. The first stage is noise reduction and it aims to enhance the phoneme signals by excluding the unvoiced signals and keep only the voiced signal. Wiener filter is adapted to accomplish this task. The second stage is based on Mel-Frequency Cepstral Coefficients method to extract a vector of features to represent each phoneme signal. Eventually, pattern recognition neural network is designed as recognizer. The proposed system produces sufficient outcomes with 20 hidden neurons. Keywords—Arabic; Malay; pattern recognition; Wiener; Mel- Frequency Cepstral Coefficients I. INTRODUCTION It is well known the wide range of Artificial Neural Networks (ANN) application in speech recognition, financial, telecommunications, electronics and medical. As for speech recognition system for Arabic language, (1) & (2) have addressed the implementation of recognition system based on Arabic native speakers. In general, neural networks (NNs) for phonemes recognition are divided into several categories for instance Very Large Scale Integration (VLSI) NN that can be divided into digital and analogue with digital NNs being more compatible with feed-forward neural networks while analogue NNs are found to be more successful with recurrent NNs (3). From Automatic Speech System (ASR) point of view, NNs for recognition purpose can be divided into two main category namely conventional neural networks such as Multilayer Perceptron (MLP) and Radial Basis Function (RBF) and secondly Recurrent Neural Networks (RNN). The first category of NNs was implemented as pattern classifiers and proven capable for recognition purpose but not at par as compared to Hidden Markov Model (HMM) performance (2). At present, HMM has been proven to be the most successful approach in ASR research area until recently the possibility to hybrid both HMM and ANN (4) & (5). In general, speech recognition systems consist of three stages specifically noise reduction, feature extraction and classification. The aim of noise reduction is to reduce the level of noise in the speech signal and make it more compatible for the ASR. As for feature extraction, this process attempted to extract the most relevant features related to the phoneme signal. The extracted features acted as input features of the NN during training and testing. Finally, the purpose of classification process is to arrange the most similar and related features in categories based on the training phase. Basically, networks training database are being observed and updated during training session of the networks, involving the weights and biases arguments. Networks with lower errors can be considered as better networks (6). Hence, this paper deems to explore further the ability of NN for Arabic phonemes recognition spoken by Malay individuals. This paper is arranged as following; related work is discussed in section II followed by section III that discussed method to be implemented based on Wiener filter, Mel-Frequency Cepstral Coefficients and pattern recognition neural network. Section IV elaborated experimental analysis and results and followed by Conclusion section.

Speech is a skill that most of the time we take for granted. In reality, this ability is a complex mechanism which requires thoughts to be translated into words who are further transposed into sounds, a mechanism which involves precise coordination of several muscles and joints. In some cases, this complex mechanism can no longer be performed and may be accompanied by almost complete loss of motor activity such in diseases as: stroke, Lock-Down syndrome, amyotrophic lateral sclerosis, cerebral palsy etc. The most recent method that aims to supplement the speech mechanism is imaginary speech recognition using electroencephalographic (EEG) signals, by using complex computing mechanisms like Deep Learning (DL) in order to decode the thoughts. In this paper we aim to recognize three types of clustered phonemes using conventional speech recognition techniques, like Mel-Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) combined with Convolutional Neural Networks (CNN). We compared four types of features extraction: MFCC, LPC, MFCC+ LPC combined into 1-channel matrix and MFCC+ LPC combined into a 2-channel matrix. We showed that MFCC coefficients offer a better accuracy than LPC and that concatenating MFCC and LPC into a 2-channel matrix we obtain a better performance than combining them into 1-channel matrix.

This paper addresses the problem of reconstructing wideband speech signals from observed narrowband speech signals. The goal of this work is to improve the perceived quality of speech signals which have been transmitted through narrowband channels or degraded during acquisition. We describe a system, based on linear predictive coding, for estimating wideband speech from narrowband. This system employs both previously identified and novel techniques. Experimental results are provided in order to illustrate the system's ability to improve speech quality. Both objective and subjective criteria are used to evaluate the quality of the processed speech signals.

Electronic speech coding had its origin in 1928 when Homer Dudley proposed to send speech signals over a new transatlantic telegraph cable with the (then) enormous bandwidth of 100 Hz. Dudley argued that speech was generated by slowly moving articulators and should therefore require only some 100-Hz total bandwidth for transmission. Since extraction of these parameters proved difficult, Dudley suggested, as an alternative, the transmission of the likewise slowly changing spectral information. The result was the channel vocoder, which (together with formant and voice-excited vocoders) dominated speech coding and that of musical sounds of single-pitch instruments until the mid 1960s. Unhappy with the vocoder's “electronic accent.” Bishnu Atal and the author, in 1967, advanced the idea of linear predictive coding (LPC), motivated by the desire to replace rigid vocoderlike coding by a more flexible method in which a “prediction residual” could represent deviations of real speech signals from an assumed all-pole model. In 1972, subjective error criteria were introduced, based on auditory models of masking and the possibility of “hiding” quantizing noise under the speech signal. The resulting code-excited linear predictive (CELP) coders based on statistical models have been operated at 1/4 bit per sample for the residual at virtually no audible degradation. With powerful digital signal processing chips, CELP has now become the method of choice for synthetic-voice applications. For musical sounds, masking models and linear predictive coding, which is equivalent to prewhitening of the spectrum, lead to a substantial reduction in the required number of bits per sample.