Digital Speech Processing Research Articles

Modern Standard Arabic speech disorders corpus for digital speech processing applications

Modern Standard Arabic speech disorders corpus for digital speech processing applications

A measure of differences in speech signals by the voice timbre

This research relates to the field of speech technologies, where the key problem is the optimization of speech signal processing under conditions of a priori uncertainty of its fine structure. The task of automatic (objective) analysis of voice timbre using a speech signal of finite duration is considered. It is proposed to use a universal information-theoretic approach to solve it. Based on the Kullback-Leibler divergence, an expression is obtained for the asymptotically optimal decision statistic for distinguishing speech signals by voice timbre. Pointed to an acute problem in its practical implementation, namely: synchronization of the sequence of observations with the main tone of speech signals. To overcome the described problem, an objective measure of timbre differences in speech signals is proposed in terms of the acoustic theory of speech production and its model of the speaker’s vocal tract of the “acoustic trumpet” type. The possibilities of practical implementation of a new measure based on an adaptive recursive are considered. A full-scale experiment was set up and carried out. According to its results, two main properties of the proposed measure were confirmed: high sensitivity to differences in speech signals in terms of voice timbre and, at the same time, invariance with respect to the pitch frequency. The results obtained can be used in the design and research of digital speech processing systems tuned to the speaker’s voice, for example, digital speech transmission systems, biometric, biomedical systems, etc.

English

Wireless communication has become increasingly popular in recent years due to its mobility, portability and efficient service. Quality Voice Communication is the basic need of all such devices. Due to the significant spectrum demand, the bandwidth allocated to Professional Radio devices is decreasing. This results in lowering voice/ speech quality. Due to limited bandwidth, the design of Radio is changing. The main change is shifting from Analogue signal processing to Digital signal processing of speech and radio signals. The conversion of analogue-natured speech to digital form itself creates loss and faithfulness. Such Radio includes a voice coder for analogue to digital conversion of speech. Human spoken language, speaker (male or female), and pronunciation play an essential role in direct voice and Radio communication. It is the primary object which decides the design of low bandwidth coder. This paper deals with Marathi language characteristics and the interdependence of language constructs that affects voice/speech quality. In this paper, we extracted features of the Marathi language using PRAAT software and analyzed its constructs using statistical tools. The actual testing on Radio is carried out. The speech quality observed during such testing is correlated with statistical results. It is shown that the standard deviation in pitch and formants in male speech is less (average 35%) compared to the standard deviation in pitch and formants in female speech. This results in lowering the quality of male speech compared to female speech. It is also shown that some vowels and combinations of certain vowels and consonants produce poor speech quality compared to others.

Speech Coding Using Discrete Cosine Transform and Chaotic Map

Recently, data of multimedia performs an exponentially blowing tendency, saturating daily life of humans. Various modalities of data, includes images, texts and video, plays important role in different aspects and has wide. However, the key problem of utilizing data of large scale is cost of processing and massive storage. Therefore, for efficient communications and for economical storage requires effective techniques of data compression to reduce the volume of data. Speech coding is a main problem in the area of digital speech processing. The process of converting the voice signals into a more compressed form is speech coding. In this work, we demonstrate that a DCT with a chaotic system combined with run-length coding can be utilized to implement speech coding of very low bit-rate with high reconstruction quality. Experimental result show that compression ratio is about 13% when implemented on Librispeech dataset.

MilVAD: A bag-level MNIST modelling of voice activity detection using deep multiple instance learning

Voice Activity Detection (VAD) which is used as an onset step for majority of the applications in Digital Speech Processing (DSP) area is defined as the process of identifying speech region in an audio recording. It is mostly used for automatic speech recognition, speaker identification/verification, speech enhancement, speaker diarization etc. in order to reduce output errors and increase overall effectiveness of the systems. In this study, a bag-level MNIST modelling of VAD was proposed using Deep Multiple Instance Learning (Deep MIL) approach. To the best of our knowledge, because this is the first attempt that the VAD was modelled as a MIL problem in the literature, we named “milVAD”. The MNIST dataset was modified to obtain bag-level classifier model for the VAD framework while the MIL algorithm was implemented inside a Convolutional Neural Network (CNN) as an embedded layer using Noisy-And pooling method. The proposed modelling scenario has surprisingly achieved high training accuracy, which is approx. 99.91%, with only nine epochs via Deep MIL at bag-level. These results proved that the MIL can efficiently be used for the VAD systems in the manner of binary classification.

A NOVEL SPEECH RECOGNITION SYSTEM USING FUZZY NEURAL NETWORK

An important field in digital speech processing is human voice. Speech recognition in humans has long been a hot topic in signal processing and artificial intelligence research. Natural Language Processing (NLP) offers an interdisciplinary subfield called speech recognition that makes it easier for machines to recognize spoken language and transform it into text. To communicate with machines, speech recognition machines has the potential to be helpful. Technology that can have communicates in real time has been made possible. However, there remain additional issues, such as speaker variance due to other factors like age, gender, signal speed, pronunciation variations, noise from the surrounding area etc. Classification of age and gender is important for speech processing. A lot of work has been done to enhance each of these phases in order to get better and more accurate results. The main goal of this analysis is on the integration of machine learning into the speech recognition system. Hence, this analysis presents a novel speech recognition system using Fuzzy neural network. Pre-processing, speech signal segmentation, speech feature extraction, and speaker recognition are the different phases of a speech recognition system. It uses a fuzzy neural network to identify the speaker's age and gender. Accuracy, F1-score, and Precision are used to evaluate the fuzzy model's performance.

Speaker Identification in Different Emotional States in Arabic and English

Speaker recognition is an important application of digital speech processing. However, a major challenge degrading the robustness of speaker-recognition systems is variation in the emotional states of speakers, such as happiness, anger, sadness, or surprise. In this paper, we propose a speaker recognition system corresponding to three states, namely emotional, neutral, and with no consideration for a speaker's state (i.e., the speaker can be in an emotional state or neutral state), for two languages: Arabic and English. Additionally, cross-language speaker recognition was applied in emotional, neutral, and (emotional + neutral) states. Convolutional neural network and long short-term memory models were used to design a convolutional recurrent neural network (CRNN) main system. We also investigated the use of linearly spaced spectrograms as speech-feature inputs. The proposed system utilizes the KSUEmotions, emotional prosody speech and transcripts, WEST POINT, and TIMIT corpora. The CRNN system exhibited accuracies as high as 97.4% and 97.18% for Arabic and English emotional speech inputs, respectively, and 99.89% and 99.4% for Arabic and English neutral speech inputs, respectively. For the cross-language program, the overall CRNN system accuracy was as high as 91.83%, 99.88%, and 95.36% for emotional, neutral, and (emotional + neutral) states, respectively.

Fuzzy Logic Based Voice Recognition As Per Their Gender And Age Group

Human voice is the very important field in digital speech processing. In the coming technologies every system will be based on the human voice. The system will be locked and unlocked by human voice. In this research, we classify the voice according to their age groups. We can recognize the voice by calculating many various parameters such as pitch, power amplitude and THD and many more. There are many approaches through which we can recognize the gender’s voice such as Hidden Markov Model [HMM], Dynamic Time Warping [DTW] etc. This paper is basically depending on the voice perception system which helps in to differentiate the voice between the gender’s voice and also differentiate the voice depends on age gp to 12yrs children voice with the use of SVM and Gaussian membership function.

Fuzzy Logic Based Voice Recognition as per Their Gender and Age Group

Human voice is the very important field in digital speech processing. In the coming technologies every system will be based on the human voice. The system will be locked and unlocked by human voice. In this research, we classify the voice ac-cording to their age groups. We can recognize the voice by calculating many various parameters such as pitch, power amplitude and THD and many more. There are many approaches through which we can recognize the gender’s voice such as Hidden Markov Model [HMM], Dynamic Time Warping [DTW] etc. This paper is basically depending on the voice perception system which helps in to differentiate the voice between the gender’s voice and also differentiate the voice depends on age gp to 12yrs children voice with the use of SVM and Gaussian membership function

Turkish vowel classification based on acoustical and decompositional features optimized by Genetic Algorithm

In digital speech processing (DSP), vowel classification is one of the important and challenging issue of automatic speech/speaker recognition (ASR) systems where vowels need to be processed in order to identify spoken speech or speaker identity. Because the vowels’ acoustic features such as Formant Frequencies, Energy, Zero Crossing Rate (ZCR) etc. contain patterns revealing speech/speaker characteristics, vowels were used in many studies like speech/speaker recognition, speaker verification, accent/dialect estimation, illness detection via speech etc. In this work, digital speech signals of 8 vowels (/a/, /e/, /ı/, /i/, /o/, /ö/, /u/ and /ü/) in Turkish language were classified using a feature set optimized by Genetic Algorithm. The entire feature vector, which has length of 24, comprises acoustical features (Formant Frequency obtained by Linear Predictive Coding-LPC, Energy, ZCR, Mel-Frequency Cepstral Coefficients-MFCCs) and decompositional features (Wavelet Decomposition Shannon Entropy). All of the 8 isolated vowels were uttered by 10 male speakers (only male to avoid gender effect), and totally 2762 observations were obtained from the vowels signals for classification step. The results inferred that the feature vector optimized by Genetic Algorithm method reached 100% classification success accuracy via the simple 1NN classifier with Manhattan distance. These findings proved success of the proposed model and the Genetic Algorithm’s efficiency on feature selection for the Turkish vowel classification.

Distinctive Phonetic Features Modeling and Extraction Using Deep Neural Networks

Feature extraction is a critical stage of digital speech processing systems. Quality of features is of great importance to provide a solid foundation upon which the subsequent stages stand. Distinctive phonetic features (DPFs) are one of the most representative features of the speech signals. The significance of DPFs is in their ability to provide abstract description of the places and manners of articulation of the language phonemes. A phoneme's DPF element reflects unique articulatory information about that phoneme. Therefore, there is a need to discover and investigate each DPF element individually in order to achieve a deeper understanding and to come up with a descriptive model for each one. Such fine-grained modeling will satisfy the uniqueness of each DPF element. In this paper, the problem of DPF modeling and extraction of modern standard Arabic is tackled. Due to the remarkable success of deep neural networks (DNNs) that are initialized using deep belief networks (DBNs) in serving DSP applications and its capability of extracting highly representative features from the raw data, we exploit its modeling power to investigate and model the DPF elements. DNN models are compared with the classical multilayer perceptron (MLP) models. The representativeness of several acoustic cues for different DPF elements was also measured. This paper is based on formalizing DPF modeling problem as a binary classification problem. Because the DPF elements are highly imbalanced data, evaluating the quality of models is a very tricky process. This paper addresses the proper evaluation measures satisfying the imbalanced nature of the DPF elements. After modeling each element individually, the two top-level DPF extractors are designed: MLP- and DNN-based extractors. The results show the quality of DNN models and their superiority over MLPs with accuracies of 89.0% and 86.7%, respectively.

Nova metoda za prepoznavanje aktivnosti ljudi zasnovana na IMU senzorima i na teoriji digitalne obrade govora

Nova metoda za prepoznavanje aktivnosti ljudi zasnovana na IMU senzorima i na teoriji digitalne obrade govora

Perceptual Significance of Cepstral Distortion Measures in Digital Speech Processing

Currently, one of the most widely used distance measures in speech and speaker recognition is the Euclidean distance between mel frequency cepstral coefficients (MFCC). MFCCs are based on filter bank algorithm whose filters are equally spaced on a perceptually motivated mel frequency scale. The value of mel cepstral vector, as well as the properties of the corresponding cepstral distance, are determined by several parameters used in mel cepstral analysis. The aim of this work is to examine compatibility of MFCC measure with human perception for different values of parameters in the analysis. By analysing mel filter bank parameters it is found that filter bank with 24 bands, 220 mels bandwidth and band overlap coefficient equal and higher than one gives optimal spectral distortion (SD) distance measures. For this kind of mel filter bank, the difference between vowels can be recognised for full-length mel cepstral SD RMS measure higher than 0.4 - 0.5 dB. Further on, we will show that usage of truncated mel cepstral vector (12 coefficients) is justified for speech recognition, but may be arguable for speaker recognition. We also analysed the impact of aliasing in cepstral domain on cepstral distortion measures. The results showed high correlation of SD distances calculated from aperiodic and periodic mel cepstrum, leading to the conclusion that the impact of aliasing is generally minor. There are rare exceptions where aliasing is present, and these were also analysed.

A Review on Feature Extraction Techniques for Speech Processing

Speech and language are considered uniquely human abilities Speech is a complex signal that is characterized by varying distributions of energy in time as well as in frequency, depending on the specific sound that is being produced. The aim of digital speech processing is to take advantage of digital computing techniques to process the speech signal for increased understanding, improved communication, and increased efficiency and Definition of various types of speech classes, feature extraction techniques, speech classifiers and performance evaluation are issues that requires attention in designing of speech processing system.

Intelligent Integrated Home Security System Using Raspberry Pi

Security, be it of a small apartment or of a sophisticated, gigantic institute is of arrant concern. In metro cities in India, for a housing complex/small apartment, security personnel are generally employed for the said purpose, who may not be that efficient especially at night. This paper intends to build an “Intelligent Home Security System” based on Digital Image Processing and Speech Processing, using a Raspberry Pi. The system is divided into two sub- systems. 1. Allowing/Disallowing vehicles based on Number Plate Recognition2. Allowing/Disallowing human beings based on Face Detection and Recognition and Speech RecognitionA database of the residents of the building is prepared. It consists of a pre-recorded security code word and an image of the resident. A separate vehicular database containing the number plates of the cars is also stored in the memory.

Speech Coding Techniques

Abstract Speech coding has been major issue in the area of digital speech processing. Speech coding is the process of transforming the speech signal in a more compressed form, which can then be transmitted with few numbers of binary digits. It is not possible to access unlimited bandwidth of a channel each time we send a signal across it which leads to code and compress speech signals. Speech compression is applied in long distance communication, high-class speech storage, and message encryption. Speech coding is a lossy type of coding and hence the output signal does not exactly sound like the input. Speech coding techniques discussed here are Linear predictive coding, waveform coding, Code excited linear predictive coding, etc. Linear Predictive Coding and Code Excited Linear Predictive Coding techniques are studied with the help of MATLAB to check their performance measures like compression ratio and speech audible quality.

USING DIGITAL PROCESSING OF SPEECH AND VIDEO TO SUPPORT INTERACTION BETWEEN THE DEAF COMMUNITY AND NORMAL PEOPLE

This paper presents a framework for a proposed pattern recognition system to support communication between the deaf people and the normal. The presented system is basedon the digital processing of both video and speech. Firstly, it receives the gesture video from the deaf person and converts it to the corresponding spoken word. Secondly, it receives the spoken word from the normal person and converts it to the corresponding gesture. The proposed system comprises the following functions: (1) signal pre-processing; (2) signal analysis; and (3) training and testing. Fig. 1 shows the main block diagram of the proposed system. Keywords: sign language, Wavelet Packet Decomposition (WPD), Invariant moment, Mel-Frequency Cepstral Coefficients (MFCC), Timbral texture, Weighted Euclidean distance(WED)

HIGH SPEED CARRY SAVE MULTIPLIER BASED LINEAR CONVOLUTION USING VEDIC MATHAMATICS

VLSI applications include Digital Signal Processing, Digital control systems, Telecommunications, Speech and Audio processing for audiology and speech language pathology. The latest research in VLSI is the design and implementation of DSP systems which are essential for above applications. The fundamental computation in DSP Systems is convolution. Convolution and LTI systems are the heart and soul of DSP. The behavior of LTI systems in continuous time is described by Convolution integral whereas the behavior in discrete-time is described by Linear convolution. In this paper, Linear convolution is performed using carry save multiplier architecture based on vertical and cross wise algorithm of Urdhva – Tiryagbhyam in Vedic mathematics. Coding is done using Verilog HDL(verilog Hardware Description Language). Simulation and Synthesis are performed using Xilinx FPGA

Wavelet adaptation for automatic voice disorders sorting

Early diagnosis of voice disorders and abnormalities by means of digital speech processing is a subject of interest for many researchers. Various methods are introduced in the literature, some of which are able to extensively discriminate pathological voices from normal ones. Voice disorders sorting, on the other hand, has received less attention due to the complexity of the problem. Although, previous publications show satisfactory results in classifying one type of disordered voice from normal cases, or two different types of abnormalities from each other, no comprehensive approach for automatic sorting of vocal abnormalities has been offered yet.In this paper, a solution for this problem is suggested. We create a powerful wavelet feature extraction approach, in which, instead of standard wavelets, adaptive wavelets are generated and applied to the voice signals. Orthogonal wavelets are parameterized via lattice structure and then, the optimal parameters are investigated through an iterative process, using the genetic algorithm (GA). GA is guided by the classifier results. Based on the generated wavelet, a wavelet-filterbank is constructed and the voice signals are decomposed to compute eight energy-based features. A support vector machine (SVM) then classifies the signals using the extracted features.Experimental results show that six various types of vocal disorders: paralysis, nodules, polyps, edema, spasmodic dysphonia and keratosis are fully sorted via the proposed method. This could be a successful step toward sorting a larger number of abnormalities associated with the vocal system.

Review of distinctive phonetic features and the Arabic share in related modern research

Most research in the field of digital speech technology has traditionally been conducted in only a few languages, such as English, French, Spanish, or Chinese. Numerous studies using distinctive phonetic features (DPFs) with different techniques and algorithms have been carried out during the last 3 decades, mainly in English, Japanese, and other languages of industrialized countries. DPF elements are based on a technique used by linguists and digital speech and language experts to distinguish between different phones by considering the lowest level of actual features during phonation. These studies have investigated the best performances, outcomes, and theories, especially those regarding digital speech recognition. The aim of this paper is to present the background of DPF theories and the usefulness thereof for digital speech and language processing. In addition, we highlight the background of Arabic language phonology compared to 2 well-known languages to enhance the current knowledge about this narrow language discipline. Finally, this work reviews the research dealing with DPF strategies for digital speech and language processing using computing and engineering techniques and theories. Based on the literature search conducted for this paper, we conclude that although the Arabic language is a very important and old Semitic language, hitherto it has suffered from a lack of modern research resources and theories on DPF elements.