Journal articles: 'Speech - Signal Processing'

1

Honda, Masaaki, and Takehiro Moriya. "Speech signal processing system." Journal of the Acoustical Society of America 89, no. 1 (January 1991): 491. http://dx.doi.org/10.1121/1.400434.

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Yoo, Hah-Young. "Method for processing speech signal in speech processing system." Journal of the Acoustical Society of America 103, no. 4 (April 1998): 1699. http://dx.doi.org/10.1121/1.421327.

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Ibragimova, Sayora. "THE ADVANTAGE OFTHEWAVELET TRANSFORM IN PROCESSING OF SPEECH SIGNALS." Technical Sciences 4, no. 3 (March 30, 2021): 37–41. http://dx.doi.org/10.26739/2181-9696-2021-3-6.

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This work deals with basic theory of wavelet transform and multi-scale analysis of speech signals, briefly reviewed the main differences between wavelet transform and Fourier transform in the analysis of speech signals. The possibilities to use the method of wavelet analysis to speech recognition systems and its main advantages. In most existing systems of recognition and analysis of speech sound considered as a stream of vectors whose elements are some frequency response. Therefore, the speech processing in real time using sequential algorithms requires computing resources with high performance. Examples of how this method can be used when processing speech signals and build standards for systems of recognition.Key words: digital signal processing, Fourier transform, wavelet analysis, speech signal, wavelet transform

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Kane, Joi, and Akira Nohara. "Speech signal processing apparatus for extracting a speech signal from a noisy speech signal." Journal of the Acoustical Society of America 96, no. 1 (July 1994): 619. http://dx.doi.org/10.1121/1.410421.

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Sawusch, James R. "Seeing Speech: Signal Processing in Speech Research." Contemporary Psychology: A Journal of Reviews 32, no. 3 (March 1987): 280–81. http://dx.doi.org/10.1037/026934.

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Schimmel, Claude, and Stan Tempelaars. "Signal Processing, Speech, and Music." Computer Music Journal 21, no. 3 (1997): 101. http://dx.doi.org/10.2307/3681021.

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YEGNANARAYANA, B. "Speech Communication and Signal Processing." Sadhana 36, no. 5 (October 2011): 551–53. http://dx.doi.org/10.1007/s12046-011-0037-1.

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de Abreu, Caio Cesar Enside, Marco Aparecido Queiroz Duarte, Bruno Rodrigues de Oliveira, Jozue Vieira Filho, and Francisco Villarreal. "Regression-Based Noise Modeling for Speech Signal Processing." Fluctuation and Noise Letters 20, no. 03 (January 30, 2021): 2150022. http://dx.doi.org/10.1142/s021947752150022x.

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Speech processing systems are very important in different applications involving speech and voice quality such as automatic speech recognition, forensic phonetics and speech enhancement, among others. In most of them, the acoustic environmental noise is added to the original signal, decreasing the signal-to-noise ratio (SNR) and the speech quality by consequence. Therefore, estimating noise is one of the most important steps in speech processing whether to reduce it before processing or to design robust algorithms. In this paper, a new approach to estimate noise from speech signals is presented and its effectiveness is tested in the speech enhancement context. For this purpose, partial least squares (PLS) regression is used to model the acoustic environment (AE) and a Wiener filter based on a priori SNR estimation is implemented to evaluate the proposed approach. Six noise types are used to create seven acoustically modeled noises. The basic idea is to consider the AE model to identify the noise type and estimate its power to be used in a speech processing system. Speech signals processed using the proposed method and classical noise estimators are evaluated through objective measures. Results show that the proposed method produces better speech quality than state-of-the-art noise estimators, enabling it to be used in real-time applications in the field of robotic, telecommunications and acoustic analysis.

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Mehrzad, M., M. D. Abolhassani, A. H. Jafari, J. Alirezaie, and M. Sangargir. "Cochlear Implant Speech Processing Using Wavelet Transform." ISRN Signal Processing 2012 (August 1, 2012): 1–6. http://dx.doi.org/10.5402/2012/628706.

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We present a method for coding speech signals for the simulation of a cochlear implant. The method is based on a wavelet packet decomposition strategy. We used wavelet packet db4 for 7 levels, generated a series of channels with bandwidths exactly the same as nucleus device, and applied an input stimulus to each channel. The processed signal was then reconstructed and compared to the original signal, which preserved the contents to a high percentage. Finally, performance of the wavelet packet decomposition in terms of computational complexity was compared to other commonly used strategies in cochlear implants. The results showed the power of this method in processing of the input signal for implant users with less complexity than other methods, while maintaining the contents of the input signal to a very good extent.

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Rahman, Md Saifur. "Book Review: Signal Processing of Speech:." International Journal of Electrical Engineering & Education 31, no. 1 (January 1994): 89–90. http://dx.doi.org/10.1177/002072099403100117.

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Wang, Jhing‐Fa, Shi‐Huang Chen, and Jyh‐Shing Shyuu. "Wavelet transforms for speech signal processing." Journal of the Chinese Institute of Engineers 22, no. 5 (July 1999): 549–60. http://dx.doi.org/10.1080/02533839.1999.9670493.

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Pena, M. "Signal-Driven Computations in Speech Processing." Science 298, no. 5593 (August 29, 2002): 604–7. http://dx.doi.org/10.1126/science.1072901.

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Hirose, Keikichi. "Speech signal processing using optical method." Speech Communication 13, no. 1-2 (October 1993): 223–29. http://dx.doi.org/10.1016/0167-6393(93)90073-t.

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Erell, Adoram, and Avi Kleinstein. "Audio signal processing for speech communication." Journal of the Acoustical Society of America 121, no. 6 (2007): 3269. http://dx.doi.org/10.1121/1.2748575.

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S.Hallikar, Rohini, M. Uttarakumari, Padmaraju K, and Yashas D. "Modified Turbo and SDROM Method for Speech Processing for Cochlear Implants." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 179. http://dx.doi.org/10.14419/ijet.v7i4.5.20040.

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A performance comparison of Signal Dependent Rank Order Mean (SDROM) method of speech signal enhancement with a speech enhancement method which makes use of a Turbo combination and SDROM filter referred to as modified Turbo and SDROM technique is made in this paper. Normally, speech signals are used as inputs to a cochlear implant signal processing unit.Sounds are corrupted by different noises such as AWGN, Impulsive noise and babble. The results are evaluated in terms of enhancements evaluations done by basically three parameters namely correlation coefficient, log spectral distortion (LSD) and segmental signal to noise ratio(SSNR). These parameters are calculated between the processed and the clean signals.. Results prove the superior performance of the new method especially for AWGN corrupted speech.

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Abdusalomov, Akmalbek Bobomirzaevich, Furkat Safarov, Mekhriddin Rakhimov, Boburkhon Turaev, and Taeg Keun Whangbo. "Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm." Sensors 22, no. 21 (October 24, 2022): 8122. http://dx.doi.org/10.3390/s22218122.

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Speech recognition refers to the capability of software or hardware to receive a speech signal, identify the speaker’s features in the speech signal, and recognize the speaker thereafter. In general, the speech recognition process involves three main steps: acoustic processing, feature extraction, and classification/recognition. The purpose of feature extraction is to illustrate a speech signal using a predetermined number of signal components. This is because all information in the acoustic signal is excessively cumbersome to handle, and some information is irrelevant in the identification task. This study proposes a machine learning-based approach that performs feature parameter extraction from speech signals to improve the performance of speech recognition applications in real-time smart city environments. Moreover, the principle of mapping a block of main memory to the cache is used efficiently to reduce computing time. The block size of cache memory is a parameter that strongly affects the cache performance. In particular, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in speech recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from speech signals. Problems with overclocking during the digital processing of speech signals have yet to be completely resolved. The experimental results demonstrate that the proposed method successfully extracts the signal features and achieves seamless classification performance compared to other conventional speech recognition algorithms.

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Marciniak, T., R. Weychan, A. Stankiewicz, and A. Dąbrowski. "Biometric speech signal processing in a system with digital signal processor." Bulletin of the Polish Academy of Sciences Technical Sciences 62, no. 3 (September 1, 2014): 589–94. http://dx.doi.org/10.2478/bpasts-2014-0064.

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Abstract This paper presents an analysis of issues related to the fixed-point implementation of a speech signal applied to biometric purposes. For preparing the system for automatic speaker identification and for experimental tests we have used the Matlab computing environment and the development software for Texas Instruments digital signal processors, namely the Code Composer Studio (CCS). The tested speech signals have been processed with the TMS320C5515 processor. The paper examines limitations associated with operation of the realized embedded system, demonstrates advantages and disadvantages of the technique of automatic software conversion from Matlab to the CCS and shows the impact of the fixed-point representation on the speech identification effectiveness.

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Gao, Bo, Zi Ming Kou, and Hong Wei Yan. "Research on Speaker Recognition Based on Wavelet Analysis and Search Tree." Advanced Materials Research 159 (December 2010): 68–71. http://dx.doi.org/10.4028/www.scientific.net/amr.159.68.

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Speaker Recognition (SR) is an important branch of speech recognition. The current speech signal processing in SR uses short-time processing technique, namely assuming speech signals are short-time stationary. But in fact, the speech signal is non-stationary. The wavelet analysis is a kind of new analyzing tool and is suitable for analyzing non-stationary signal, which has achieved impressive results in the field of signal coding. Based on this, the wavelet analysis theory was introduced into SR research to improve the traditional speech segmentation methods and characteristics parameters. In order to speed the recognition, a kind of SR model based on search tree was also brought out.

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Hu, J., C. C. Cheng, and W. H. Liu. "Processing of speech signals using a microphone array for intelligent robots." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 219, no. 2 (March 1, 2005): 133–43. http://dx.doi.org/10.1243/095965105x9461.

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For intelligent robots to interact with people, an efficient human-robot communication interface is very important (e.g. voice command). However, recognizing voice command or speech represents only part of speech communication. The physics of speech signals includes other information, such as speaker direction. Secondly, a basic element of processing the speech signal is recognition at the acoustic level. However, the performance of recognition depends greatly on the reception. In a noisy environment, the success rate can be very poor. As a result, prior to speech recognition, it is important to process the speech signals to extract the needed content while rejecting others (such as background noise). This paper presents a speech purification system for robots to improve the signal-to-noise ratio of reception and an algorithm with a multidirection calibration beamformer.

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20

Ney, H. "Sprachverarbeitung und sprachuebertragung (speech processing and speech transmission)." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 5 (October 1985): 1346–47. http://dx.doi.org/10.1109/tassp.1985.1164673.

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21

B, Nagesh, and Dr M. Uttara Kumari. "A Review on Machine Learning for Audio Applications." Journal of University of Shanghai for Science and Technology 23, no. 07 (June 30, 2021): 62–70. http://dx.doi.org/10.51201/jusst/21/06508.

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Audio processing is an important branch under the signal processing domain. It deals with the manipulation of the audio signals to achieve a task like filtering, data compression, speech processing, noise suppression, etc. which improves the quality of the audio signal. For applications such as natural language processing, speech generation, automatic speech recognition, the conventional algorithms aren’t sufficient. There is a need for machine learning or deep learning algorithms which can be implemented so that the audio signal processing can be achieved with good results and accuracy. In this paper, a review of the various algorithms used by researchers in the past has been described and gives the appropriate algorithm that can be used for the respective applications.

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22

Fulop, Sean A., Kelly Fitz, and Douglas O'Shaughnessy. "Signal Processing in Speech and Hearing Technology." Acoustics Today 7, no. 3 (2011): 25. http://dx.doi.org/10.1121/1.3658272.

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23

Nakamura, Makoto. "Integrated digital circuit for processing speech signal." Journal of the Acoustical Society of America 87, no. 4 (April 1990): 1831. http://dx.doi.org/10.1121/1.399365.

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Miyazawa, Kouki. "Introduction to Speech Signal Processing Using MATLAB." Journal of The Institute of Image Information and Television Engineers 66, no. 2 (2012): 130–33. http://dx.doi.org/10.3169/itej.66.130.

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25

Suzuki, Ryoji. "Apparatus and method for speech signal processing." Journal of the Acoustical Society of America 98, no. 5 (November 1995): 2404. http://dx.doi.org/10.1121/1.413267.

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Hardwick, John C., and Jae S. Lim. "Processing a speech signal with estimated pitch." Journal of the Acoustical Society of America 96, no. 1 (July 1994): 619. http://dx.doi.org/10.1121/1.410420.

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ASANO, F. "Signal Processing Techniques for Robust Speech Recognition." IEICE Transactions on Information and Systems E91-D, no. 3 (March 1, 2008): 393–401. http://dx.doi.org/10.1093/ietisy/e91-d.3.393.

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Rabiner, Lawrence R., and Ronald W. Schafer. "Introduction to Digital Speech Processing." Foundations and Trends® in Signal Processing 1, no. 1–2 (2007): 1–194. http://dx.doi.org/10.1561/2000000001.

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Delic, Vlado, Darko Pekar, Radovan Obradovic, and Milan Secujski. "Speech signal processing in ASR&TTS algorithms." Facta universitatis - series: Electronics and Energetics 16, no. 3 (2003): 355–64. http://dx.doi.org/10.2298/fuee0303355d.

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Speech signal processing and modeling in systems for continuous speech recognition and Text-to-Speech synthesis in Serbian language are described in this paper. Both systems are fully developed by the authors and do not use any third party software. Accuracy of the speech recognizer and intelligibility of the TTS system are in the range of the best solutions in the world, and all conditions are met for commercial use of these solutions.

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Hauth, Christopher F., Simon C. Berning, Birger Kollmeier, and Thomas Brand. "Modeling Binaural Unmasking of Speech Using a Blind Binaural Processing Stage." Trends in Hearing 24 (January 2020): 233121652097563. http://dx.doi.org/10.1177/2331216520975630.

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The equalization cancellation model is often used to predict the binaural masking level difference. Previously its application to speech in noise has required separate knowledge about the speech and noise signals to maximize the signal-to-noise ratio (SNR). Here, a novel, blind equalization cancellation model is introduced that can use the mixed signals. This approach does not require any assumptions about particular sound source directions. It uses different strategies for positive and negative SNRs, with the switching between the two steered by a blind decision stage utilizing modulation cues. The output of the model is a single-channel signal with enhanced SNR, which we analyzed using the speech intelligibility index to compare speech intelligibility predictions. In a first experiment, the model was tested on experimental data obtained in a scenario with spatially separated target and masker signals. Predicted speech recognition thresholds were in good agreement with measured speech recognition thresholds with a root mean square error less than 1 dB. A second experiment investigated signals at positive SNRs, which was achieved using time compressed and low-pass filtered speech. The results demonstrated that binaural unmasking of speech occurs at positive SNRs and that the modulation-based switching strategy can predict the experimental results.

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Ma, Lina, and Yanjie Lei. "Optimization of Computer Aided English Pronunciation Teaching System Based on Speech Signal Processing Technology." Computer-Aided Design and Applications 18, S3 (October 20, 2020): 129–40. http://dx.doi.org/10.14733/cadaps.2021.s3.129-140.

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After the development of speech signal processing technology has matured, various language learning tools have begun to emerge. The speech signal processing technology has many functions, such as standard tape reading, making audio aids, synthesizing speech, and performing speech evaluation. Therefore, the adoption of speech signal processing technology in English pronunciation teaching can meet different teaching needs. Voice signal processing technology can present teaching information in different forms, and promote multi-form communication between teachers and students, and between students and students. This will help stimulate students' interest in learning English and improve the overall teaching level of English pronunciation. This research first investigates and studies the current level of English pronunciation mastery. After combining the relevant principles of speech signal processing technology, it puts forward the areas that need to be optimized in the design of the English pronunciation teaching system. Through the demand analysis and function analysis of the system, this research uses speech signal processing technology to extract the characteristics of the speech signal---Mel Frequency Cepstrum Coefficient (MFCC), The system's speech signal preprocessing, speech signal feature extraction and dynamic time warping (DTW) recognition algorithms are optimized. At the same time, this research combines multimedia teaching resources such as text, pronunciation video and excellent courses to study the realization process of each function of the system.

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Zafar, Shakeel, Imran Fareed Nizami, Mobeen Ur Rehman, Muhammad Majid, and Jihyoung Ryu. "NISQE: Non-Intrusive Speech Quality Evaluator Based on Natural Statistics of Mean Subtracted Contrast Normalized Coefficients of Spectrogram." Sensors 23, no. 12 (June 16, 2023): 5652. http://dx.doi.org/10.3390/s23125652.

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With the evolution in technology, communication based on the voice has gained importance in applications such as online conferencing, online meetings, voice-over internet protocol (VoIP), etc. Limiting factors such as environmental noise, encoding and decoding of the speech signal, and limitations of technology may degrade the quality of the speech signal. Therefore, there is a requirement for continuous quality assessment of the speech signal. Speech quality assessment (SQA) enables the system to automatically tune network parameters to improve speech quality. Furthermore, there are many speech transmitters and receivers that are used for voice processing including mobile devices and high-performance computers that can benefit from SQA. SQA plays a significant role in the evaluation of speech-processing systems. Non-intrusive speech quality assessment (NI-SQA) is a challenging task due to the unavailability of pristine speech signals in real-world scenarios. The success of NI-SQA techniques highly relies on the features used to assess speech quality. Various NI-SQA methods are available that extract features from speech signals in different domains, but they do not take into account the natural structure of the speech signals for assessment of speech quality. This work proposes a method for NI-SQA based on the natural structure of the speech signals that are approximated using the natural spectrogram statistical (NSS) properties derived from the speech signal spectrogram. The pristine version of the speech signal follows a structured natural pattern that is disrupted when distortion is introduced in the speech signal. The deviation of NSS properties between the pristine and distorted speech signals is utilized to predict speech quality. The proposed methodology shows better performance in comparison to state-of-the-art NI-SQA methods on the Centre for Speech Technology Voice Cloning Toolkit corpus (VCTK-Corpus) with a Spearman’s rank-ordered correlation constant (SRC) of 0.902, Pearson correlation constant (PCC) of 0.960, and root mean squared error (RMSE) of 0.206. Conversely, on the NOIZEUS-960 database, the proposed methodology shows an SRC of 0.958, PCC of 0.960, and RMSE of 0.114.

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Schum, Donald J. "Noise reduction via signal processing." Hearing Journal 56, no. 5 (May 2003): 27–32. http://dx.doi.org/10.1097/01.hj.0000293885.26777.b5.

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Zheng, Jian, and Tian De Gao. "A Dual-DSP Sonobuoy Signal Processing System." Applied Mechanics and Materials 571-572 (June 2014): 873–77. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.873.

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Sonobuoy is used as aviation antisubmarine device to detect submarines, and its wireless communication mechanism would introduce radio interference. The speech signal needs to be identified from the submarine noise in order to facilitate sonar signal processing system to do further processing of the signal. This paper presents a TS-201 based dual-DSP sonobuoy signal processing system, and proposes an algorithm using Cubic Spline Interpolation and Pearson correlation coefficient to identify the speech signal from submarine radiated noise signal. This article describes the specific signal processing algorithm of the system, the hardware and software design of the system. This article uses a large number of data from experiments to test the hardware and software systems separately. The results of tests are analyzed, which indicate that the system function well in identifying speech signal from submarine radiated noise signal to, with a percentage of 98% correct rate.

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Nakamura, Makio. "Speech codec and a method of processing a speech signal with speech codec." Journal of the Acoustical Society of America 102, no. 2 (August 1997): 683. http://dx.doi.org/10.1121/1.419926.

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Dasarathy, Belur V. "Robust speech processing." Information Fusion 5, no. 2 (June 2004): 75. http://dx.doi.org/10.1016/j.inffus.2004.02.002.

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Neuburg, Edward P. "Artificial choral speech: Using digital signal processing algorithms in speech research." Journal of the Acoustical Society of America 80, S1 (December 1986): S96. http://dx.doi.org/10.1121/1.2024066.

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Naithani, Deeksha. "Development of a Real-Time Audio Signal Processing System for Speech Enhancement." Mathematical Statistician and Engineering Applications 70, no. 2 (February 26, 2021): 1041–52. http://dx.doi.org/10.17762/msea.v70i2.2157.

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The requirements of real-time signal processing dictate that the audio signal must be completely processed before the subsequent audio segment may be received. This is done in order to achieve those requirements. This highlights how important it is to create methods of signal processing that are not just quick but also accurate. I describe many ways for processing audio signals in real time within the scope of this thesis. The publications that are being presented cover a wide range of issues, including noise dosimetry, speech analysis, and network echo cancellation, to name a few. In this article, the process of constructing a system that uses audio signal processing to improve speech in real time is broken down and examined. Speech enhancement makes speech more audible and comprehensible in noisy surroundings, which is beneficial to individuals who have hearing loss as well as speech recognition and communication systems. Signal-to-noise ratio, power spectral efficiency ratio, and spectrum transfer entropy index are the metrics that are utilized to evaluate speech quality enhancement system development strategies, abbreviated as STOI. Because studies have shown that being exposed to loud noises over extended periods of time can have severe impacts on health, it is essential to have precise methods for detecting the levels of noise. The findings of a study that measured exposure to noise while also taking into account the impact of the speaker's own voice are presented in this article.

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He, Juan, and Shuai Kang. "Design of Speech Signal Acquisition and Processing System." Advanced Materials Research 645 (January 2013): 188–91. http://dx.doi.org/10.4028/www.scientific.net/amr.645.188.

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The design of a speech signal acquisition and processing system is introduced. The collected data is analyzed and processed Using the powerful Matlab software, and this system is verified through the example.The system of mobile communication provides theoretical reference value in high quality speech communication service .

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King, Robin W. "Speech Signal Analysis, Synthesis and Recognition Exercises Using Matlab." International Journal of Electrical Engineering & Education 34, no. 2 (April 1997): 161–72. http://dx.doi.org/10.1177/002072099703400206.

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Three MATLAB exercises covering speech signal analysis and principles of linear prediction, formant synthesis and speech recognition are described. These exercises, which are assessed components in an elective course on speech and language processing, enable undergraduate electrical engineering students to explore fundamentally important concepts in speech science and signal processing.

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Auti, Dr Nisha, Atharva Pujari, Anagha Desai, Shreya Patil, Sanika Kshirsagar, and Rutika Rindhe. "Advanced Audio Signal Processing for Speaker Recognition and Sentiment Analysis." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1717–24. http://dx.doi.org/10.22214/ijraset.2023.51825.

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Abstract: Automatic Speech Recognition (ASR) technology has revolutionized human-computer interaction by allowing users to communicate with computer interfaces using their voice in a natural way. Speaker recognition is a biometric recognition method that identifies individuals based on their unique speech signal, with potential applications in security, communication, and personalization. Sentiment analysis is a statistical method that analyzes unique acoustic properties of the speaker's voice to identify emotions or sentiments in speech. This allows for automated speech recognition systems to accurately categorize speech as Positive, Neutral, or Negative. While sentiment analysis has been developed for various languages, further research is required for regional languages. This project aims to improve the accuracy of automatic speech recognition systems by implementing advanced audio signal processing and sentiment analysis detection. The proposed system will identify the speaker's voice and analyze the audio signal to detect the context of speech, including the identification of foul language and aggressive speech. The system will be developed for the Marathi Language dataset, with potential for further development in other languages.

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Diesch, Eugen. "SpeechLab: PC software for digital speech signal processing." Behavior Research Methods, Instruments, & Computers 29, no. 2 (June 1997): 302. http://dx.doi.org/10.3758/bf03204831.

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Islam Molla, Md Khademul, Somlal Das, Md Ekramul Hamid, and Keikichi Hirose. "Empirical Mode Decomposition for Advanced Speech Signal Processing." Journal of Signal Processing 17, no. 6 (2013): 215–29. http://dx.doi.org/10.2299/jsp.17.215.

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Zhang, Jun Hong, and Zhi Tan. "Speech Signal Acquisition and Processing Based on DSP." Advanced Materials Research 588-589 (November 2012): 830–33. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.830.

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The design method of software and hardware was introduced about speech signal acquisition and process based on DSP chip TMS320C64X produced by TI company in this paper. The principle figure of the system, hardware interface circuits, software flow chart as well as relational experiment data and waves were also present.

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Nelson, Peggy, Janet Rutledge, and Juan Carlos Tejero‐Calado. "Signal processing algorithms for speech in fluctuating noise." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3166. http://dx.doi.org/10.1121/1.2933223.

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Nefti, Samir, and Olivier Boeffard. "Method of automatic processing of a speech signal." Journal of the Acoustical Society of America 125, no. 5 (2009): 3487. http://dx.doi.org/10.1121/1.3139579.

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Tychkov, A. Yu, A. K. Alimuradov, and P. P. Churakov. "Adaptive Signal Processing Method for Speech Organ Diagnostics." Measurement Techniques 59, no. 5 (August 2016): 485–90. http://dx.doi.org/10.1007/s11018-016-0994-1.

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Allen, J., and Feipeng Li. "Speech perception and cochlear signal processing [Life Sciences]." IEEE Signal Processing Magazine 26, no. 4 (July 2009): 73–77. http://dx.doi.org/10.1109/msp.2009.932564.

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Hamid, Md Ekramul, Md Khademul Islam Molla, Xin Dang, and Takayoshi Nakai. "Single Channel Speech Enhancement Using Adaptive Soft-Thresholding with Bivariate EMD." ISRN Signal Processing 2013 (July 31, 2013): 1–8. http://dx.doi.org/10.1155/2013/724378.

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This paper presents a novel data adaptive thresholding approach to single channel speech enhancement. The noisy speech signal and fractional Gaussian noise (fGn) are combined to produce the complex signal. The fGn is generated using the noise variance roughly estimated from the noisy speech signal. Bivariate empirical mode decomposition (bEMD) is employed to decompose the complex signal into a finite number of complex-valued intrinsic mode functions (IMFs). The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. Each IMF is divided into short time frames for local processing. The variance of IMF of fGn calculated within a frame is used as the reference term to classify corresponding noisy speech frame into noise and signal dominant frames. Only the noise dominant frames are soft-thresholded to reduce the noise effects. Then, all the frames as well as IMFs of speech are combined, yielding the enhanced speech signal. The experimental results show the improved performance of the proposed algorithm compared to the recently reported methods.

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Subbaiah, Putta Venkata, and Hima Deepthi. "A Study to Analyze Enhancement Techniques on Sound Quality for Bone Conduction and Air Conduction Speech Processing." Scalable Computing: Practice and Experience 21, no. 1 (March 19, 2020): 57–62. http://dx.doi.org/10.12694/scpe.v21i1.1612.

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In recent years, enhancing speech signal for communications become more complex due to the environmental noises for military and navel applications. Many researchers analyzed and developed complex algorithms and expressed ways to enhance speech signal for both Bone Conducted Speech (BCS) and Air Conducted Speech (ACS) processing techniques. BCS signal is vigorous for outer environmental noises and with low quality signal. ACS signal is sensitive for environmental noises nut rigid in amplitude and high frequencies. Here we have reviewed and investigated various articles to explore their ideas in speech processing for enhancing intelligibility of sound quality.

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Journal articles on the topic 'Speech - Signal Processing'

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