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Добірка наукової літератури з теми "Speech processing systems"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 30 липня 2024

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Статті в журналах з теми "Speech processing systems"

1

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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2

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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3

Thompson, Laura A., and William C. Ogden. "Visible speech improves human language understanding: Implications for speech processing systems." Artificial Intelligence Review 9, no. 4-5 (October 1995): 347–58. http://dx.doi.org/10.1007/bf00849044.

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4

Scott, Sophie K., and Carolyn McGettigan. "The neural processing of masked speech." Hearing Research 303 (September 2013): 58–66. http://dx.doi.org/10.1016/j.heares.2013.05.001.

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5

M Tasbolatov, N. Mekebayev, O. Mamyrbayev, M. Turdalyuly, D. Oralbekova,. "Algorithms and architectures of speech recognition systems." Psychology and Education Journal 58, no. 2 (February 20, 2021): 6497–501. http://dx.doi.org/10.17762/pae.v58i2.3182.

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Digital processing of speech signal and the voice recognition algorithm is very important for fast and accurate automatic scoring of the recognition technology. A voice is a signal of infinite information. The direct analysis and synthesis of a complex speech signal is due to the fact that the information is contained in the signal. Speech is the most natural way of communicating people. The task of speech recognition is to convert speech into a sequence of words using a computer program. This article presents an algorithm of extracting MFCC for speech recognition. The MFCC algorithm reduces the processing power by 53% compared to the conventional algorithm. Automatic speech recognition using Matlab.

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6

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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7

FUNAKOSHI, KOTARO, TAKENOBU TOKUNAGA, and HOZUMI TANAKA. "Processing Japanese Self-correction in Speech Dialog Systems." Journal of Natural Language Processing 10, no. 4 (2003): 33–53. http://dx.doi.org/10.5715/jnlp.10.4_33.

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8

Hills, A., and K. Scott. "Perceived degradation effects in packet speech systems." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 5 (May 1987): 699–701. http://dx.doi.org/10.1109/tassp.1987.1165187.

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9

Gransier, Robin, and Jan Wouters. "Neural auditory processing of parameterized speech envelopes." Hearing Research 412 (December 2021): 108374. http://dx.doi.org/10.1016/j.heares.2021.108374.

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10

Moon, Todd K., Jacob H. Gunther, Cortnie Broadus, Wendy Hou, and Nils Nelson. "Turbo Processing for Speech Recognition." IEEE Transactions on Cybernetics 44, no. 1 (January 2014): 83–91. http://dx.doi.org/10.1109/tcyb.2013.2247593.

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Дисертації з теми "Speech processing systems"

1

Coetzee, H. J. "The development of a new objective speech quality measure for speech coding applications." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/15474.

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2

Morris, Robert W. "Enhancement and recognition of whispered speech." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180338/unrestricted/morris%5frobert%5fw%5f200312%5fphd.pdf.

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3

Quackenbush, Schuyler Reynier. "Objective measures of speech quality." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/13376.

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4

Lucey, Simon. "Audio-visual speech processing." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36172/7/SimonLuceyPhDThesis.pdf.

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Speech is inherently bimodal, relying on cues from the acoustic and visual speech modalities for perception. The McGurk effect demonstrates that when humans are presented with conflicting acoustic and visual stimuli, the perceived sound may not exist in either modality. This effect has formed the basis for modelling the complementary nature of acoustic and visual speech by encapsulating them into the relatively new research field of audio-visual speech processing (AVSP). Traditional acoustic based speech processing systems have attained a high level of performance in recent years, but the performance of these systems is heavily dependent on a match between training and testing conditions. In the presence of mismatched conditions (eg. acoustic noise) the performance of acoustic speech processing applications can degrade markedly. AVSP aims to increase the robustness and performance of conventional speech processing applications through the integration of the acoustic and visual modalities of speech, in particular the tasks of isolated word speech and text-dependent speaker recognition. Two major problems in AVSP are addressed in this thesis, the first of which concerns the extraction of pertinent visual features for effective speech reading and visual speaker recognition. Appropriate representations of the mouth are explored for improved classification performance for speech and speaker recognition. Secondly, there is the question of how to effectively integrate the acoustic and visual speech modalities for robust and improved performance. This question is explored in-depth using hidden Markov model(HMM)classifiers. The development and investigation of integration strategies for AVSP required research into a new branch of pattern recognition known as classifier combination theory. A novel framework is presented for optimally combining classifiers so their combined performance is greater than any of those classifiers individually. The benefits of this framework are not restricted to AVSP, as they can be applied to any task where there is a need for combining independent classifiers.

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5

Chiou, Fred Y. "User-interactive speech enhancement using fuzzy logic." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/14916.

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6

陳我智 and Ngor-chi Chan. "Text-to-speech conversion for Putonghua." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209580.

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7

Barger, Peter James. "Speech processing for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36081/1/36081_Barger_1998.pdf.

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This thesis examines speech processing systems appropriate for use in forensic analysis. The need for automatic speech processing systems for forensic use is justified by the increasing use of electronically recorded speech for communication. An automatic speaker identification and verification system is described which was tested on data gathered by the Queensland Police Force. Speaker identification using Gaussian mixture models (GMMs) is shown to be useful as an indicator of identity, but not sufficiently accurate to be used as the sole means of identification. It is shown that training GMMs on speech of one language and testing on speech of another language introduces significant bias into the results, which is unpredictable in its effects. This has implications for the performance of the system on subjects attempting to disguise their voices. Automatic gender identification systems are shown to be highly accurate, attaining 98% accuracy, even with very simple classifiers, and when tested on speech degraded by coding or reverberation. These gender gates are useful as initial classifiers in a larger speaker classification system and may even find independent use in a forensic environment. A dual microphone method of improving the performance of speaker identification systems in noisy environments is described. The method gives a significant improvement in log-likelihood scores when its output is used as input to a GMM. This implies that speaker identification tests may be improved in accuracy. A method of automatically assessing the quality of transmitted speech segments using a classification scheme is described. By classifying the difference between cepstral parameters describing the original speech and the transmitted speech, an estimate of the speech quality is obtained.

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8

Yatrou, Paul M. "Analysis of predictor mistracking in ADPCM speech coders." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66242.

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9

Fang, Jie. "Design of secure speech encryption systems." Thesis, Queensland University of Technology, 1990. https://eprints.qut.edu.au/36471/1/36471_Fang_1990.pdf.

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This thesis investigates the design of digital speech encryption systems based on low bit rate vocoders. The speech quality and the cryptographic strength of the system are determined by vocoder and encryptor respectively. Three different low bit rate vocoders, 2400 BPS LPC ( Linear Prediction Coding) vocoder, 9600 BPS MELPC (Mul tipulse Excited Linear Prediction Coding) vocoder and 4800 BPS CELP (Codebook Excited Linear Prediction coding) vocoder, have been simulated. The performances of these vocoders are evaluated by using four objective measures. The thesis considers the follows aspects of digital encryption system: * Security * Speech quality * Robustness * System delay Several choices of the cryptosystem for the encryption of digital speech are investigated, and the performance of the overall system is discussed. The work presented in this thesis enables a secure communication system designer to select a speech coding scheme and a cipher system to meet required level of security and speech quality. encryption systems throughout this thesis refers to mathematics analysis and simulation of such systems rather than the actual construction of electronic circuits.

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10

Liu, Zhu Lin. "Speech synthesis via adaptive Fourier decomposition." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493215.

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Книги з теми "Speech processing systems"

1

Sinha, Priyabrata. Speech Processing in Embedded Systems. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-75581-6.

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2

W, Parsons Thomas. Voice and speech processing. New York: McGraw-Hill, 1986.

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3

Wang, Jhing-Fa. Real World Speech Processing. Boston, MA: Springer US, 2004.

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4

Frank, Fallside, and Woods William A, eds. Computer speech processing. Englewood Cliffs, (N.J.): Prentice-Hall, 1985.

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5

Tetschner, Walt. Voice processing. 2nd ed. Boston: Artech House, 1993.

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6

Rabiner, Lawrence R. Introduction to digital speech processing. Boston, Mass: Now, 2007.

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7

Rabiner, Lawrence R. Introduction to digital speech processing. Boston, Massachusetts: Now, 2007.

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8

Rabiner, Lawrence R. Introduction to digital speech processing. Boston, Mass: Now, 2007.

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9

Sabourin, Conrad. Computational speech processing: Bibliography. Montréal, Qué: Infolingua, 1994.

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10

Sabourin, Conrad. Computational speech processing: Bibliography. Montréal: Infolingua Inc., 1994.

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Частини книг з теми "Speech processing systems"

1

Frerking, Marvin E. "Speech Processing." In Digital Signal Processing in Communication Systems, 490–547. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-4990-8_9.

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2

Néel, Françoise D., and Wolfgang M. Minker. "Multimodal Speech Systems." In Computational Models of Speech Pattern Processing, 404–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_34.

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3

Sinha, Priyabrata. "Speech Recognition." In Speech Processing in Embedded Systems, 143–55. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_10.

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4

Sinha, Priyabrata. "Speech Synthesis." In Speech Processing in Embedded Systems, 157–64. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_11.

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5

Quast, Holger, and Robert Bosch. "Speech Dialogue Systems and Natural Language Processing." In Computer Speech, 67–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-06384-2_4.

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6

Sinha, Priyabrata. "Basic Speech Processing Concepts." In Speech Processing in Embedded Systems, 37–54. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_3.

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7

Sinha, Priyabrata. "Peripherals for Speech Processing." In Speech Processing in Embedded Systems, 75–91. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_5.

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8

Sinha, Priyabrata. "Signal Processing Fundamentals." In Speech Processing in Embedded Systems, 9–36. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_2.

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9

Sinha, Priyabrata. "Speech Compression Overview." In Speech Processing in Embedded Systems, 93–100. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_6.

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10

Suendermann, David, and Roberto Pieraccini. "Crowdsourcing for Industrial Spoken Dialog Systems." In Crowdsourcing for Speech Processing, 280–302. Oxford, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118541241.ch10.

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Тези доповідей конференцій з теми "Speech processing systems"

1

"Signal Processing. Speech and Audio Processing." In 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854416.

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2

"Session WA2b: Speech processing." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335704.

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3

"Session WA8b1 Speech Processing." In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. IEEE, 2004. http://dx.doi.org/10.1109/acssc.2004.1399566.

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4

Maina, Ciira wa, and John MacLaren Walsh. "Approximate Bayesian robust speech processing." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190027.

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5

Nguyen Thanh Chung and Tang Jung Low. "Speech processing in Java-based PC speech commanding application." In 2007 International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2007. http://dx.doi.org/10.1109/icias.2007.4658590.

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6

Hansen, John H. L., Jay Plucienkowski, Stephen Gallant, Bryan Pellom, and Wayne Ward. "CU-move: robust speech processing for in-vehicle speech systems." In 6th International Conference on Spoken Language Processing (ICSLP 2000). ISCA: ISCA, 2000. http://dx.doi.org/10.21437/icslp.2000-130.

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7

Katharina Fuchs, Anna, Clemens Amon, and Martin Hagmüller. "Speech/Non-Speech Detection for Electro-Larynx Speech Using EMG." In International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005181401380144.

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8

Ogunfunmi, Tokunbo. "Session TP7b: Speech processing and speech recognition (invited) [breaker page]." In 2012 46th Asilomar Conference on Signals, Systems and Computers. IEEE, 2012. http://dx.doi.org/10.1109/acssc.2012.6489301.

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9

Black, Alan W., Ralf D. Brown, Robert Frederking, Kevin Lenzo, John Moody, Alexander I. Rudnicky, Rita Singh, and Eric Steinbrecher. "Rapid development of speech-to-speech translation systems." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA: ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-504.

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"Session WA6a: Speech processing I." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5469739.

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Звіти організацій з теми "Speech processing systems"

1

Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.

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2

Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.

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3

Bilgutay, Nihat M. Computer Facilities for High-Speed Data Acquisition, Signal Processing and Large Scale System Simulation. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada170935.

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4

Wada, Yasutaka. Working Paper PUEAA No. 3. Parallel Processing and Parallelizing Compilation Techniques for "Green Computing". Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.001r.2022.

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The fourth technological revolution has brought great advances in manufacturing processes and human communications. Although processors have become increasingly efficient, both in speed, capacity and energy consumption, their functionality regarding this last point has yet to improve. The latest innovations represent an opportunity to create "green computing" and not only more environmentally friendly electronics and software, but also to use their new efficiency to improve our daily activities, as well as the designs of our cities themselves to make them more environmentally sustainable. These new computerized systems must also be applied in accordance with the socioeconomic factors that must be taken into account in order to be modified in favor of sustainability and efficiency.

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5

Lane, Lerose, and DingXin Cheng. Pavement Condition Survey using Drone Technology. Mineta Transportation Institute, June 2023. http://dx.doi.org/10.31979/mti.2023.2202.

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Timely repairs of pavement defects are essential in protecting both public road and highway systems. Identification of pavement distresses is necessary for planning pavement repairs. This has previously been performed by engineers surveying the roadways visually in the field. As drone usage has progressed, it has become clear that drones are a valuable tool to enhance visual documentation, improve project communication, and provide various data for processing. The use of drone technology has improved both the speed and accuracy of capturing data. Available software has allowed the data to be processed and analyzed in an office environment. This report summarizes the use of drone technology for pavement evaluation for three case studies. Results from this study can be used to deepen understanding of drone use in the process of data gathering for timely repairs for transportation infrastructure.

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6

Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM. - Logging/capturing data tips - Techniques to identify 'bad' data - Methods of mapping equipment and associated regressions - Tips for pre-processing data for regressions - Machine learning tips - Establishing alarm limits - Identifying equipment problems - Multiple case studies Who Should Attend? - Data analysts - Equipment support specialists - Those interested in learning more about 'big data' and 'machine learning' Recommended Pre-reading: - PR-309-11202-R01 Field Demonstration Test of Advanced Engine and Compressor Diagnostics for CORE - PR-312-12210-R01 CEPM Monitoring Plan for 2SLB Reciprocating Engines* - PR-309-13208-R01 Field Demonstration of Integrated System and Expert Level Continuous Performance Monitoring for CORE* - PR-309-14209-R01 Field Demo of Integrated Expert Level Continuous Performance Monitoring - PR-309-15205-R01 Continuous Engine Performance Monitoring Technical Specification - PR-000-15208-R01 Reciprocating Engine Speed Stability as a Measure of Combustion Stability - PR-309-15209-R01 Evaluation of NSCR Specific Models for Use in CEPM - PR-000-16209-R01 Demonstration of Continuous Equipment Performance Monitoring - PR-015-17606-Z02 Elbow Meter Test Results* *Documents available to PRCI member only Attendance will be limited to the first 500 registrants to join the webinar. All remaining registrants will receive a link to view the recording after the webinar. Not able to attend? Register anyway to automatically receive a link to the recording after the webinar to view at your convenience! After registering, you will receive a confirmation email containing information about joining the webinar. Please visit our website for other webinars that may be of interest to you!

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.

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Murphy, Keire, and Anne Sheridan. Annual report on migration and asylum 2022: Ireland. ESRI, November 2023. http://dx.doi.org/10.26504/sustat124.

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Annual Report on Migration and Asylum gives overview of statistics and developments in migration in 2022. The European Migration Network (EMN) Ireland within the ESRI has published its annual review of migration and asylum in Ireland. The EMN is an EU network that provides objective, comparable policy-relevant information on migration and international protection. EMN Ireland is located in the ESRI and is funded by the European Union and the Department of Justice. With an overview of the latest data as well as policy and operational developments, research, and case law from 2022, this report is a comprehensive reference that gives an opportunity to view the entire migration landscape in Ireland. The report shows that many forms of migration are recovering quickly from COVID-19 travel restrictions. It also shows that migration is being impacted by shortages in the labour market and the Russian invasion of Ukraine. As a result of these developments and others, Ireland saw a significant increase in immigration, with 141,600 people arriving in the year leading up to April 2023, according to CSO figures. This represents a 31% increase from the year to April 2022. However, emigration also increased, with 64,000 individuals leaving Ireland during the same period, marking a 14% increase from the previous year. 2022 saw a significant increase in first residence permits (which are granted to migrants from outside the EEA) from 2021. 85,793 permits were issued in 2022, with education the most common reason for permits (48%). Partially reflecting changes to eligible occupations for employment permits, the number of employment permits issued was the highest in the last 10 years. 39,995 employment permits were issued, with the information and communication sector the largest recipient of permits. Key developments in this area highlighted by the report include discussions on and progress with the Employment Permits Bill, changes to the Atypical Working Scheme, plans for a single application procedure for employment permits and immigration permissions, and changes to employment permits occupation lists to respond to labour market shortages. The report analyses international protection, showing significant increases in international protection applications as well as details of applications, decisions made, and statuses awarded. It shows an expansion of decision-making in response to increased applications. Looking at the broader EU situation, the report shows that applications for international protection in Ireland accounted for 1.3% of the EU total in 2022. The report also details the pressure on the reception and accommodation system for international protection applicants and beneficiaries of temporary protection, as well as the extraordinary measures taken to scale these up. It highlights measures taken to implement the White Paper to End Direct Provision and informs on a review of timelines of the plan. It discusses changes made by the International Protection Office to speed up processing, and criticism of these measures by NGOs, as well as details of the regularisation scheme for undocumented migrants and the humanitarian admission of Afghans. The Temporary Protection Directive – an EU Directive that creates an exceptional measure to provide immediate and temporary protection in the event of a mass influx of displaced persons – was triggered for the first time in March 2022, following the Russian invasion of Ukraine. As a result, the report includes a dedicated chapter with statistics relating to arrivals and a detailed overview of Ireland’s response to displaced persons from Ukraine. It also gives a comprehensive overview of other areas of migration, as well as research and case law from 2022, providing a crucial reference text for anyone working in the area.

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Issues in Data Processing and Relevant Population Selection. OSAC Speaker Recognition Subcommittee, November 2022. http://dx.doi.org/10.29325/osac.tg.0006.

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In Forensic Automatic Speaker Recognition (FASR), forensic examiners typically compare audio recordings of a speaker whose identity is in question with recordings of known speakers to assist investigators and triers of fact in a legal proceeding. The performance of automated speaker recognition (SR) systems used for this purpose depends largely on the characteristics of the speech samples being compared. Examiners must understand the requirements of specific systems in use as well as the audio characteristics that impact system performance. Mismatch conditions between the known and questioned data samples are of particular importance, but the need for, and impact of, audio pre-processing must also be understood. The data selected for use in a relevant population can also be critical to the performance of the system. This document describes issues that arise in the processing of case data and in the selections of a relevant population for purposes of conducting an examination using a human supervised automatic speaker recognition approach in a forensic context. The document is intended to comply with the Organization of Scientific Area Committees (OSAC) for Forensic Science Technical Guidance Document.

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