Articoli di riviste: "Speech reinforcement"

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Crespo, Joao B., e Richard C. Hendriks. "Multizone Speech Reinforcement". IEEE/ACM Transactions on Audio, Speech, and Language Processing 22, n. 1 (gennaio 2014): 54–66. http://dx.doi.org/10.1109/tasl.2013.2283100.

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Gibson, Jerry, e Hoontaek Oh. "A Reinforcement Learning Approach to Speech Coding". Information 13, n. 7 (11 luglio 2022): 331. http://dx.doi.org/10.3390/info13070331.

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Abstract (sommario):

Speech coding is an essential technology for digital cellular communications, voice over IP, and video conferencing systems. For more than 25 years, the main approach to speech coding for these applications has been block-based analysis-by-synthesis linear predictive coding. An alternative approach that has been less successful is sample-by-sample tree coding of speech. We reformulate this latter approach as a multistage reinforcement learning problem with L step lookahead that incorporates exploration and exploitation to adapt model parameters and to control the speech analysis/synthesis process on a sample-by-sample basis. The minimization of the spectrally shaped reconstruction error to finite depth manages complexity and serves as an effective stand in for the overall subjective evaluation of reconstructed speech quality and intelligibility. Different control policies that attempt to persistently excite the system states and that encourage exploration are studied and evaluated. The resulting methods produce reconstructed speech quality competitive with the most popular speech codec utilized today. This new reinforcement learning formulation provides new insights and opens up new directions for system design and performance improvement.

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Mardhatillah, Elsy. "Teacher’s Reinforcement in English Classroom in MTSS Darul Makmur Sungai Cubadak". Indonesian Research Journal On Education 3, n. 1 (2 gennaio 2022): 825–32. http://dx.doi.org/10.31004/irje.v3i1.202.

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This research was due to some problems found in MTsS Darul Makmur. First, some students were not motivated in learning. Second, sometime the teacher still uses Indonesian in giving reinforcements. Third, some Students did not care about the teacher's reinforcement. This study aimed to find out the types of reinforcement used by the teacher. Then, to find out the types of reinforcement often and rarely to be usedby the teacher. Then, to find out the reasons the teacher used certain reinforcements. Last, to find out how the teacher understands the reinforcement. This research used a qualitative approach. The design of this research was descriptive because the researcher made a description of the use of reinforcement by theteacher in the English classroom. In this research, the interview and observation sheets were used by the researcher. The researcher found that the type of reinforcement used by the teacher is positive reinforcement and negative reinforcement. First, there were two types of positive reinforcement used by teachers, namely verbal reinforcement and non-verbal reinforcement. The verbal often used by theteacher was a reinforcement in the form of words and reinforcement in the form of phrases. Then, verbal reinforcement in the form of sentences was never done by the teacher in the learning process. While the non-verbal reinforcement often used by the teacher was gestural, activity reinforcement, and proximity reinforcement. Second, the negative reinforcement often used by the teacher was a warning, gesture, and eye contact. Meanwhile, the negative reinforcement rarely used by the teacher was speech volume andpunishment. Third, the reasons teachers reinforce learning are to motivate students and make students feel appreciated and happy while learning.

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CHOI, Jae-Hun, Joon-Hyuk CHANG e Seong-Ro LEE. "Efficient Speech Reinforcement Based on Low-Bit-Rate Speech Coding Parameters". IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, n. 9 (2010): 1684–87. http://dx.doi.org/10.1587/transfun.e93.a.1684.

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Alkaher, Yehav, e Israel Cohen. "Temporal Howling Detector for Speech Reinforcement Systems". Acoustics 4, n. 4 (15 novembre 2022): 967–95. http://dx.doi.org/10.3390/acoustics4040060.

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In this paper, we address the problem of howling detection in speech reinforcement system applications for utilization in howling control mechanisms. A general speech reinforcement system acquires speech from a speaker’s microphone, and delivers a reinforced speech to other listeners in the same room, or another room, through loudspeakers. The amount of gain that can be applied to the acquired speech in the closed-loop system is constrained by electro-acoustic coupling in the system, manifested in howling noises appearing as a result of acoustic feedback. A howling detection algorithm aims to early detect frequency-howls in the system, before the human ear notices. The proposed algorithm includes two cascaded stages: Soft Howling Detection and Howling False-Alarm Detection. The Soft Howling Detection is based on the temporal magnitude-slope-deviation measure, identifying potential candidate frequency-howls. Inspired by the temporal approach, the Howling False-Alarm Detection stage considers the understanding of speech-signal frequency components’ magnitude behavior under different levels of acoustic feedback. A comprehensive howling detection performance evaluation process is designed, examining the proposed algorithm in terms of detection accuracy and the time it takes for detection, under a devised set of howling scenarios. The performance improvement of the proposed algorithm, with respect to a plain magnitude-slope-deviation-based method, is demonstrated by showing faster detection response times over a set of howling change-rate configurations. The two-staged proposed algorithm also provides a significant recall improvement, while improving the precision decrease via the Howling False-Alarm Detection stage.

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Ortega, A., E. Lleida e E. Masgrau. "Speech reinforcement system for car cabin communications". IEEE Transactions on Speech and Audio Processing 13, n. 5 (settembre 2005): 917–29. http://dx.doi.org/10.1109/tsa.2005.853006.

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Pak, Junhyeong, Inyong Choi, Yu Gwang Jin e Jong Won Shin. "Multichannel speech reinforcement based on binaural unmasking". Signal Processing 139 (ottobre 2017): 165–72. http://dx.doi.org/10.1016/j.sigpro.2017.04.021.

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Czyzewski, Andrzej. "Optimizing medical personnel speech recognition models using speech synthesis and reinforcement learning". Journal of the Acoustical Society of America 154, n. 4_supplement (1 ottobre 2023): A202—A203. http://dx.doi.org/10.1121/10.0023271.

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Abstract (sommario):

Text-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons. Speech can be synthesized by mimicking different accents, dialects, and speaking styles in a medical language. Reinforcement Learning (RL), in the context of ASR, can be used to optimize a model. A model can be trained to minimize errors in speech-to-text transcription, especially for technical medical terminology. In this case, the “reward” to the RL model can be negatively proportional to the number of transcription errors. The paper presents a method and experimental study from which it is concluded that the combination of TTS and RL can enable the creation of a speech recognition model suited to the specific needs of medical personnel, helping to expand the training data and optimize the model to minimize transcription errors. The learning process used reward functions based on Mean Opinion Score (MOS), a subjective metric for assessing speech quality, and Word Error Rate (WER), which evaluates the quality of speech-to-text transcription. [The Polish National Center for Research and Development (NCBR) supported the project: “ADMEDVOICE- Adaptive intelligent speech processing system of medical personnel with the structuring of test results and support of therapeutic process,” no. INFOSTRATEG4/0003/2022.]

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Cai, Shaokang, Dezhi Han, Dun Li, Zibin Zheng e Noel Crespi. "An reinforcement learning-based speech censorship chatbot system". Journal of Supercomputing 78, n. 6 (13 gennaio 2022): 8751–73. http://dx.doi.org/10.1007/s11227-021-04251-z.

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Carr, James E., e Lisa N. Britton. "Idiosyncratic effects of noncontingent reinforcement on problematic speech". Behavioral Interventions 14, n. 1 (gennaio 1999): 37–43. http://dx.doi.org/10.1002/(sici)1099-078x(199901/03)14:1<37::aid-bin28>3.0.co;2-z.

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Isaac, Samson, Khalid Haruna, Muhammad Aminu Ahmad e Rabi Mustapha. "DEEP REINFORCEMENT LEARNING WITH HIDDEN MARKOV MODEL FOR SPEECH RECOGNITION". JOURNAL OF TECHNOLOGY & INNOVATION 3, n. 1 (2023): 01–05. http://dx.doi.org/10.26480/jtin.01.2023.01.05.

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Nowadays, many applications uses speech recognition especially the field of computer science and electronics, Speech Recognition (SR) is the interpretation of words spoken into a text. It is also known as Speech-To-Text (STT) or Automatic-Speech-Recognition(ASR), or just Word-Recognition(WR). The Hidden-Markov-Model (HMM) is a type of Markov model, which means that the future state of the model depends on the current state, not on the entire history of the system and the goal of HMM is to learn a sequence of hidden states from a set of known states. The Long-Short-Time-Memory (LSTM) network is a type of Recurrent Neural Network (RNN) that can learn long-term dependencies between time steps of sequence data. The LSTM network is trained by the network in order to predict the values of subsequent time steps in a series-to-series regression. Deep Neural Network (DNN) models are better classifiers than Gaussian Mixture Models (GMMs), they can generalize much better with a smaller number of parameters over complex distributions. They model distributions of different classes jointly, called “distributed” learning, or, more properly “tied” learning. This work is aimed at developing a speech recognition model that will predict isolated speech of some selected fruits in Hausa, Igbo and Yoruba language by using the predicting power of Mel-Frequency-Cepstral-Coefficient (MFCC), LSTM and HMM algorithms. The findings of the study would improve the development of better automatic speech applications systems and would benefit the academic and research community in the field of Natural Language Processing.

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DiLuzio, Lynette. "What to Do When You Don't Know What to Do: Dealing With Challenging Behaviors in a Therapeutic Session". Perspectives on School-Based Issues 16, n. 1 (gennaio 2015): 11–14. http://dx.doi.org/10.1044/sbi16.1.11.

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Many speech-language pathologists experience students who demonstrate challenging behaviors during sessions both in the classroom and in the therapy room. Positive reinforcement and other traditional supports can ameliorate these behaviors. However, when students become used to these methods, speech-language pathologists are often at a loss and must look to other methods to work through the challenging behaviors. Themes explored in this article include changing perspectives on “behavior,” building relationships, constructing the treatment environment, and reinforcement schedules.

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Silman, Shlomo, Carol A. Silverman e Michele B. Emmer. "Central Auditory Processing Disorders and Reduced Motivation: Three Case Studies". Journal of the American Academy of Audiology 11, n. 02 (febbraio 2000): 57–63. http://dx.doi.org/10.1055/s-0042-1748010.

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AbstractThe central auditory test results for three normal-hearing children who were initially diagnosed as having a central auditory processing disorder and learning disability are presented. They were referred to the authors for second-opinion consultations. Central auditory processing retesting was performed by the authors under the condition of no reinforcement and then the condition of reinforcement with the child's favorite food, hobby, or toy. For all three cases, the central auditory test scores improved markedly bilaterally under the condition of reinforcement as compared with the condition of no reinforcement. We hypothesize that the improvement was related to increased motivation associated with the reinforcement and that these children represented false-positive results on the central auditory test battery. Large-sample studies are needed to investigate the effect of reinforcement on test performance in children with reduced central auditory test scores. Abbreviations: BFT = Binaural Fusion Test, CAPD = central auditory processing disorder, CST = Competing Sentences Test, FST = Filtered Speech Test

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Rahman, Ahmad Fadilahtur. "TUTUR PUJIAN GURU DALAM INTERAKSI PEMBELAJARAN DI KELAS". LINGUA: Journal of Language, Literature and Teaching 13, n. 1 (3 aprile 2016): 49–58. http://dx.doi.org/10.30957/lingua.v13i1.10.

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This study aimed to describe the form and function apreciation speech act in the interaction in the classroom. This study used descriptive qualitative approach. From the research found a form of apreciation speech act in the learning interaction in the form of declarative sentences, sentence introgatif, and imperative sentences. While the function of speech act earned praise for smoothing function apreciation, smoothes warning, advise, and provide reinforcement.

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AKKAPLAN, Selvet, Merve ÖZBAL BATUK e Gonca SENNAROĞLU. "İşitme cihazı kullanıcısı bebeklerin görsel pekiştirmeli konuşmayı ayırt etme yöntemi ile değerlendirilmesi: Olgu serisi". Turkish Journal of Audiology And Hearing Research 4, n. 3 (dicembre 2021): 86–90. http://dx.doi.org/10.34034/tjahr.987479.

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Evaluation of babies with hearing aids using visual reinforced ınfant speech discrimination method: A case series Speech perception measurements are an important part of the audiology test battery. It is important to investigate hearing loss in the early period and to evaluate the auditory pathway in terms of speech perception for appropriate amplification. Due to the level of language development in infants, it is not possible to objectively evaluate speech perception skills with the speech test battery used in adults. It is necessary to use a test battery that gives objective results for the measurement of speech perception in infants. Visual Reinforcement Infant Speech Discrimination (VRISD) method is the most commonly used test technique to evaluate speech discrimination skills in infants. The aim of this study is to evaluate the speech discrimination skills of 3 babies aged 6-18 months, who are hearing aid users, with the VRISD test method. In the VRISD test protocol used in this study, test stimuli were created by matching /a/ with /i/ and /ba/ with /da/ speech stimuli. These stimulus pairs are arranged as one target sound and the other background sound, and they are presented at 70 dB via loudspeaker by creating a repeating sound sequence. Animated video that attracts the attention of the baby was used as visual reinforcement. Speech discrimination skills of 3 babies with bilateral hearing aids were evaluated with the VRISD test, and the results of the cases are presented. As a result of the study, speech discrimination skills of babies with hearing aids will be evaluated with the VRISD test method. These results contribute to habilitation and amplification programming. Keywords: Hearing aid, speech perception, pediatric audiology

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Molina, Carlos, Nestor Becerra Yoma, Fernando Huenupán, Claudio Garretón e Jorge Wuth. "Maximum Entropy-Based Reinforcement Learning Using a Confidence Measure in Speech Recognition for Telephone Speech". IEEE Transactions on Audio, Speech, and Language Processing 18, n. 5 (luglio 2010): 1041–52. http://dx.doi.org/10.1109/tasl.2009.2032618.

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Lee, Kai-Fu, e Sanjoy Mahajan. "Corrective and reinforcement learning for speaker-independent continuous speech recognition". Computer Speech & Language 4, n. 3 (luglio 1990): 231–45. http://dx.doi.org/10.1016/0885-2308(90)90006-r.

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Tjandra, Andros, Sakriani Sakti e Satoshi Nakamura. "End-to-End Speech Recognition Sequence Training With Reinforcement Learning". IEEE Access 7 (2019): 79758–69. http://dx.doi.org/10.1109/access.2019.2922617.

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Shin, Jong Won, e Nam Soo Kim. "Perceptual Reinforcement of Speech Signal Based on Partial Specific Loudness". IEEE Signal Processing Letters 14, n. 11 (novembre 2007): 887–90. http://dx.doi.org/10.1109/lsp.2007.900222.

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Di Berardino, Federica, Stella Forti, Valentina Mattei, Dario Alpini e Antonio Cesarani. "Non-verbal visual reinforcement affects speech audiometry in the elderly". European Archives of Oto-Rhino-Laryngology 267, n. 9 (23 aprile 2010): 1367–70. http://dx.doi.org/10.1007/s00405-010-1254-7.

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Shitov, Denis, Elena Pirogova, Tadeusz A. Wysocki e Margaret Lech. "Deep Reinforcement Learning for Articulatory Synthesis in a Vowel-to-Vowel Imitation Task". Sensors 23, n. 7 (24 marzo 2023): 3437. http://dx.doi.org/10.3390/s23073437.

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Articulatory synthesis is one of the approaches used for modeling human speech production. In this study, we propose a model-based algorithm for learning the policy to control the vocal tract of the articulatory synthesizer in a vowel-to-vowel imitation task. Our method does not require external training data, since the policy is learned through interactions with the vocal tract model. To improve the sample efficiency of the learning, we trained the model of speech production dynamics simultaneously with the policy. The policy was trained in a supervised way using predictions of the model of speech production dynamics. To stabilize the training, early stopping was incorporated into the algorithm. Additionally, we extracted acoustic features using an acoustic word embedding (AWE) model. This model was trained to discriminate between different words and to enable compact encoding of acoustics while preserving contextual information of the input. Our preliminary experiments showed that introducing this AWE model was crucial to guide the policy toward a near-optimal solution. The acoustic embeddings, obtained using the proposed approach, were revealed to be useful when applied as inputs to the policy and the model of speech production dynamics.

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Uhler, Kristin M., Rosalinda Baca, Emily Dudas e Tammy Fredrickson. "Refining Stimulus Parameters in Assessing Infant Speech Perception Using Visual Reinforcement Infant Speech Discrimination: Sensation Level". Journal of the American Academy of Audiology 26, n. 10 (novembre 2015): 807–14. http://dx.doi.org/10.3766/jaaa.14093.

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Background: Speech perception measures have long been considered an integral piece of the audiological assessment battery. Currently, a prelinguistic, standardized measure of speech perception is missing in the clinical assessment battery for infants and young toddlers. Such a measure would allow systematic assessment of speech perception abilities of infants as well as the potential to investigate the impact early identification of hearing loss and early fitting of amplification have on the auditory pathways. Purpose: To investigate the impact of sensation level (SL) on the ability of infants with normal hearing (NH) to discriminate /a-i/ and /ba-da/ and to determine if performance on the two contrasts are significantly different in predicting the discrimination criterion. Research Design: The design was based on a survival analysis model for event occurrence and a repeated measures logistic model for binary outcomes. The outcome for survival analysis was the minimum SL for criterion and the outcome for the logistic regression model was the presence/absence of achieving the criterion. Criterion achievement was designated when an infant’s proportion correct score was >0.75 on the discrimination performance task. Study Sample: Twenty-two infants with NH sensitivity participated in this study. There were 9 males and 13 females, aged 6–14 mo. Data Collection and Analysis: Testing took place over two to three sessions. The first session consisted of a hearing test, threshold assessment of the two speech sounds (/a/ and /i/), and if time and attention allowed, visual reinforcement infant speech discrimination (VRISD). The second session consisted of VRISD assessment for the two test contrasts (/a-i/ and /ba-da/). The presentation level started at 50 dBA. If the infant was unable to successfully achieve criterion (>0.75) at 50 dBA, the presentation level was increased to 70 dBA followed by 60 dBA. Data examination included an event analysis, which provided the probability of criterion distribution across SL. The second stage of the analysis was a repeated measures logistic regression where SL and contrast were used to predict the likelihood of speech discrimination criterion. Results: Infants were able to reach criterion for the /a-i/ contrast at statistically lower SLs when compared to /ba-da/. There were six infants who never reached criterion for /ba-da/ and one never reached criterion for /a-i/. The conditional probability of not reaching criterion by 70 dB SL was 0% for /a-i/ and 21% for /ba-da/. The predictive logistic regression model showed that children were more likely to discriminate the /a-i/ even when controlling for SL. Conclusions: Nearly all normal-hearing infants can demonstrate discrimination criterion of a vowel contrast at 60 dB SL, while a level of ≥70 dB SL may be needed to allow all infants to demonstrate discrimination criterion of a difficult consonant contrast.

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Kostek, Bozena, Sebastian Laskowski e Karolina Mizgier. "Modeling and Designing Acoustical Conditions of the Interior - Case Study". Archives of Acoustics 41, n. 3 (1 settembre 2016): 473–84. http://dx.doi.org/10.1515/aoa-2016-0044.

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Abstract The primary aim of this research study was to model acoustic conditions of the Courtyard of the Gdańsk University of Technology Main Building, and then to design a sound reinforcement system for this interior. First, results of measurements of the parameters of the acoustic field are presented. Then, the comparison between measured and predicted values using the ODEON program is shown. Collected data indicate a long reverberation time which results in poor speech intelligibility. Then, a thorough analysis is perform to improve the acoustic properties of the model of the interior investigated. On the basis of the improved acoustic model two options of a sound reinforcement system for this interior are proposed, and then analyzed. After applying sound absorbing material it was noted that the predicted speech intelligibility increased from bad/poor rating to good category.

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CHOI, Jae-Hun, Woo-Sang PARK e Joon-Hyuk CHANG. "Speech Reinforcement Based on Soft Decision under Far-End Noise Environments". IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E92-A, n. 8 (2009): 2116–19. http://dx.doi.org/10.1587/transfun.e92.a.2116.

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Gayathri, R., e K. Sheela Sobana Rani. "Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification". Computer Systems Science and Engineering 46, n. 1 (2023): 43–56. http://dx.doi.org/10.32604/csse.2023.032491.

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Goodman, Julie, e Bob Remington. "Acquisition of expressive signing: comparison of reinforcement strategies". Augmentative and Alternative Communication 9, n. 1 (gennaio 1993): 26–35. http://dx.doi.org/10.1080/07434619312331276381.

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Nesteruk, Jeffrey. "Corporate Speech as Commercial Speech: A Response to Mayer's Nike V. Kasky Analysis". Business Ethics Quarterly 17, n. 1 (gennaio 2007): 97–103. http://dx.doi.org/10.5840/beq200717115.

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ABSTRACT:Raising the issue of corporate moral agency in our examination of the morality of corporate speech is important for two fundamental reasons. Each reason suggests we exercise caution in conflating corporations and individuals as the law often does. First, raising the issue of corporate moral agency is important to the aim of providing a framework for ethically evaluating corporate speech. It is tempting to proceed as if the nature of corporate speech is self-evident. But this is hardly the case. Corporations are not natural persons, and we mustn't assume corporate speech is indistinguishable from human speech. Before we can ethically evaluate corporate speech, we must first clarify what corporate speech is. This requires an understanding of the fundamental nature of the corporate entity, including its moral status. Second, raising the issue of corporate moral agency is important if we wish to promote morally responsible corporate speech. Any diminished moral capacity on the corporation's part would suggest a core role for the strong legal reinforcement of any ethical aspirations here. In promoting morally responsible corporate speech, ethical injunctions uncoupled from an effective legal regime may hold only limited promise.

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Widen, Judith. "Behavioral Screening of High-Risk Infants Using Visual Reinforcement Audiometry". Seminars in Hearing 11, n. 04 (novembre 1990): 342–55. http://dx.doi.org/10.1055/s-0028-1085514.

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Jailani, Adi Idham, Nazarul Azali Razali, Ahmad Harith Syah Md Yusuf, Ariff Imran Anuar Yatim e Nor Atifah Mohamad. "Worchitect: An English Grammar (Parts of Speech) Card Game". International Journal of Modern Languages And Applied Linguistics 2, n. 1 (4 giugno 2018): 1. http://dx.doi.org/10.24191/ijmal.v2i1.7361.

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Mastery of the English grammar is an intricate subject. Conventional teaching and learning of the English grammar have found to be an arduous task for teachers and a lacklustre one for students. The traditional pen and paper method often cause second language (L2) learners to become unmotivated in understanding this important element of the language. Thus, it is critical to provide L2 learners with the motivation to engage learning grammar in a more meaningful and purposive process. An ideal way to provide such learning experiences is through the use of language games that accommodate L2 learners’ desire to grasp grammar rules in an enjoyable way. To fill the gap for a purposive and meaningful grammar-based language game, Worchitect, a card-based game that focuses on (English) parts of speech is developed. The card game poses players/learners with questions that will foster their understanding of the rules of grammar for them to play the game and accumulate the highest scores possible. This game provides a constructive reinforcement to L2 users as it allows for the English parts of speech (and grammar) rules to be deductively attained. Furthermore, Worchitect is highly marketable as it is suitable for learners of various language proficiencies; for language teachers to be used as reinforcement or the actual learning activity; for parents who are looking to spend quality time with their children; and for any language enthusiast.

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Idemaru, Kaori, Adam A. Bramlett e Vsevolod Kapatsinski. "Learning from a single cue: Is phonetic learning dimension-based?" Journal of the Acoustical Society of America 152, n. 4 (ottobre 2022): A173. http://dx.doi.org/10.1121/10.0015928.

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Phonetic cue-weighting, the process of altering the weights of certain dimensions (e.g., F0) in the speech signal, is a fundamental process in speech perception. Cue-reweighting is the process of adaptation required for understanding new accents and learning second language speech contrasts; however, little is understood about the underlying mechanisms. Harmon et al. (2019) examined three candidate mechanisms (distributional, supervised, and reinforcement learning) showing evidence for reinforcement learning. The current study investigates Harmon et al.’s (2019) assumed phonetic dimensions by asking how a single cue in a phonetic dimension (e.g., a single voice onset time (VOT) value) of a phonological contrast ([b]/[p]) generalizes to other values of the phonetic dimension. Said simpler, is phonetic learning dimension-based? Native English listeners (N = 270) participated in an online perceptual training experiment in which participants were asked to identify word contrasts like pear and bear. Results suggest that learning to downweigh a cue (e.g., VOT = 5 ms) for [b]/[p] generalizes across new VOT values (e.g., VOT = 15 ms). However, the generalization did not extend to the most distant value (e.g., VOT = 35 ms). That is, cue-reweighting can affect a single phonetic category but does not extend to the entire phonetic dimension across category boundaries.

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Hao, Xiang, Chenglin Xu, Lei Xie e Haizhou Li. "Optimizing the Perceptual Quality of Time-Domain Speech Enhancement with Reinforcement Learning". Tsinghua Science and Technology 27, n. 6 (dicembre 2022): 939–47. http://dx.doi.org/10.26599/tst.2021.9010048.

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Chen, Chen, Yuchen Hu, Qiang Zhang, Heqing Zou, Beier Zhu e Eng Siong Chng. "Leveraging Modality-Specific Representations for Audio-Visual Speech Recognition via Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 11 (26 giugno 2023): 12607–15. http://dx.doi.org/10.1609/aaai.v37i11.26484.

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Audio-visual speech recognition (AVSR) has gained remarkable success for ameliorating the noise-robustness of speech recognition. Mainstream methods focus on fusing audio and visual inputs to obtain modality-invariant representations. However, such representations are prone to over-reliance on audio modality as it is much easier to recognize than video modality in clean conditions. As a result, the AVSR model underestimates the importance of visual stream in face of noise corruption. To this end, we leverage visual modality-specific representations to provide stable complementary information for the AVSR task. Specifically, we propose a reinforcement learning (RL) based framework called MSRL, where the agent dynamically harmonizes modality-invariant and modality-specific representations in the auto-regressive decoding process. We customize a reward function directly related to task-specific metrics (i.e., word error rate), which encourages the MSRL to effectively explore the optimal integration strategy. Experimental results on the LRS3 dataset show that the proposed method achieves state-of-the-art in both clean and various noisy conditions. Furthermore, we demonstrate the better generality of MSRL system than other baselines when test set contains unseen noises.

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Shuvarova, S., V. Sevastyanov, E. Borisova e N. Leontyeva. "the Disorders of Emotional and Volitional Spheres in Preschool Children with Speech Pathology". European Psychiatry 24, S1 (gennaio 2009): 1. http://dx.doi.org/10.1016/s0924-9338(09)71318-5.

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Objective:To study the peculiarity of emotional and volitional spheres in preschool children with speech pathology.Methods:250 children aged from 2 to 7 years old with speech pathology were examined. the following kinds of speech disorders were educed: impressive speech disorders - 9,2 %, expressive speech disorders - 31,2%, mixed speech disorders - 59,6 %. Due to the seriousness of the speech disorders the children were divided into 3 groups.Results:The educed disorders develop in the following psychopathological syndromes: neurotic, autism-like, hyperdynamic. in 62% of children with impressive speech disorders the autism-like syndrome was marked, and in 38 % both the autism-like and the hyperdynamic syndromes. in 74 % of children with expressive speech disorders the hyperdynamic syndrome was marked and in 26 % the neurotic syndrome. All the 3 syndromes were educed in children with mixed speech disorders however if in the structure of speech disorder a sensorial component prevailed then the autism-like syndrome was found. with the predominance of a motor component there were both hyperdynamic and neurotic syndromes. Direct dependence of intensity of these syndromes on seriousness of speech disorders was educed.Conclusions:The results of the research show, that children with speech pathology at preschool age have already the disorders of emotional and volitional spheres which influences the development of the personality of a child and contributes to the reinforcement of negative forms of reaction.

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Grebowicz, Margret. "Democracy and Pornography: On Speech, Rights, Privacies, and Pleasures in Conflict". Hypatia 26, n. 1 (2011): 150–65. http://dx.doi.org/10.1111/j.1527-2001.2010.01123.x.

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This article investigates the intersections of secrecy/interiority, the state, and speech/expression, and their implications for the rights of women. I propose a critique of commercial pornography that reanimates MacKinnon's claim that pornography and American democracy are in a relationship of mutual reinforcement, and incorporates poststructuralist (Lyotard, Baudrillard, and Butler) commitments to secrecy and unintelligibility, as well as their role in the production of pleasure.

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Widen, Judith E., e Gwendolyn M. OʼGrady. "Using visual reinforcement audiometry in the assessment of hearing in infants". Hearing Journal 55, n. 11 (novembre 2002): 28–36. http://dx.doi.org/10.1097/01.hj.0000324169.66607.69.

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Amalia Isnaini, Amir Mahmud e Saifullah. "WARNING DAN KONSEKWENSI HATE SPEECH DALAM AL-QUR’AN (Tafsir Tematik Ayat-Ayat “Hate Speech”)". Multicultural Islamic Education 7, n. 1 (30 ottobre 2023): 53–66. http://dx.doi.org/10.35891/ims.v7i1.4889.

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Hate speech becomes an unrest in society as a nation and state. This is due to concerns that hate speech could disrupt the harmony of society which has been running well. Hate speech seems to be increasingly finding an effective place to be carried out when social media is starting to be widely owned by people in Indonesia. In this way, hate speech becomes even more disturbing. This type of research is qualitative with data collection techniques using a library research approach. The data collection process was carried out by reading related literature, in the form of journals, books, articles and of course the Al-Qur'an as the main source in discussing hate speech and the hadith of the Prophet as a reinforcement of the Al-Qur'an. The results of this research indicate that hate speech has received strong criticism in the Al-Qur'an. In fact, the Al-Qur'an gives a cruel picture of people who tend to carry out hate speech and threaten painful torture. The consequences of hate speech are also mentioned a lot in the Al-Qur'an in the form of mentioning certain groups that were destroyed due to the violence (hate speech) carried out by their people.

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Xu, Xinmeng, Weiping Tu e Yuhong Yang. "Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 11 (26 giugno 2023): 13853–60. http://dx.doi.org/10.1609/aaai.v37i11.26622.

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Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems. However, a natural speech contains many fast-changing and relatively briefly acoustic events, therefore, capturing the most informative speech features by indiscriminately using local and non-local attention is challenged. We observe that the noise type and speech feature vary within a sequence of speech and the local and non-local can respectively process different types of corrupted speech regions. To leverage this, we propose Selector-Enhancer, a dual-attention based convolution neural network (CNN) with a feature-filter that can dynamically select regions from low-resolution speech features and feed them to local or non-local attention operations. In particular, the proposed feature-filter is trained by using reinforcement learning (RL) with a developed difficulty-regulated reward that related to network performance, model complexity and “the difficulty of the SE task”. The results show that our method achieves comparable or superior performance to existing approaches. In particular, Selector-Enhancer is effective for real-world denoising, where the number and types of noise are varies on a single noisy mixture.

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Wan, Genshun, Tingzhi Mao, Jingxuan Zhang, Hang Chen, Jianqing Gao e Zhongfu Ye. "Grammar-Supervised End-to-End Speech Recognition with Part-of-Speech Tagging and Dependency Parsing". Applied Sciences 13, n. 7 (27 marzo 2023): 4243. http://dx.doi.org/10.3390/app13074243.

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For most automatic speech recognition systems, many unacceptable hypothesis errors still make the recognition results absurd and difficult to understand. In this paper, we introduce the grammar information to improve the performance of the grammatical deviation distance and increase the readability of the hypothesis. The reinforcement of word embedding with grammar embedding is presented to intensify the grammar expression. An auxiliary text-to-grammar task is provided to improve the performance of the recognition results with the downstream task evaluation. Furthermore, the multiple evaluation methodology of grammar is used to explore an expandable usage paradigm with grammar knowledge. Experiments on the small open-source Mandarin speech corpus AISHELL-1 and large private-source Mandarin speech corpus TRANS-M tasks show that our method can perform very well with no additional data. Our method achieves relative character error rate reductions of 3.2% and 5.0%, a relative grammatical deviation distance reduction of 4.7% and 5.9% on AISHELL-1 and TRANS-M tasks, respectively. Moreover, the grammar-based mean opinion score of our method is about 4.29 and 3.20, significantly superior to the baseline of 4.11 and 3.02.

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Parmar, Ranbir S., e Zack Z. Cernovsky. "Self-Reinforcement Scores of Psychiatric Inpatients and Normal Controls". Psychological Reports 72, n. 1 (febbraio 1993): 35–38. http://dx.doi.org/10.2466/pr0.1993.72.1.35.

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Heiby's 1982 self-reinforcement questionnaire (a measure of the ability to reward oneself selectively for constructive behaviors or efforts and to nurture a positive self-image by means of a supportive internal speech) was administered to 12 normal controls (psychiatric nurses), 11 paranoid schizophrenic patients (DSM-III—R), 8 bipolar patients currently manic (DSM-III—R), and 12 inpatients treated for substance abuse (DSM-III—R). No significant mean differences were found on analysis of variance. The means of the groups of psychiatric inpatients and of normal persons are comparable to means of other samples of normals, e.g., college students or factory workers, reported in the literature.

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Moore, John Mick, Gary Thompson e Richard C. Folsom. "Auditory Responsiveness of Premature Infants Utilizing Visual Reinforcement Audiometry (VRA)". Ear and Hearing 13, n. 3 (giugno 1992): 187–94. http://dx.doi.org/10.1097/00003446-199206000-00008.

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Benabed, Ammar. "The Linguistic Practices in Social Media in the Algerian Context: between Human Relations Reinforcement and Hate Speech Proliferation". Technium Social Sciences Journal 29 (9 marzo 2022): 629–43. http://dx.doi.org/10.47577/tssj.v29i1.6090.

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The current study is an attempt to address some of the issues surrounding social media use with more emphasis on their role in reinforcing human relationships and spreading hatred speech. For the sake of presenting a comprehensive overview of the issue, a blend of quantitative and qualitative methodologies was implemented in the present study to analyse presence and incidence of hate speech and friendly relationship discourse in social media. To embark on such endeavour, a mixed- method, encompassing both a questionnaire and corpus analysis, is adopted. The targeted sample of the questionnaire’s addressees consists of social media users (n=100) from different categories of people. The second tool comprises a set of messages, comments and posts that are collected from different social media, especially Facebook. The findings revealed that hate speech, based on ethnicity, race, tribe, skin color, religion and physical appearance, is becoming prevalent among social media users, surpassing the spread of human relationships.

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Rvachew, Susan. "Speech Perception Training Can Facilitate Sound Production Learning". Journal of Speech, Language, and Hearing Research 37, n. 2 (aprile 1994): 347–57. http://dx.doi.org/10.1044/jshr.3702.347.

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This study examined the role of speech perception training in the correction of phonological errors. Twenty-seven preschoolers with phonological impairment who misarticulated /∫/ were randomly assigned to one of three groups: Group 1 children listened to a variety of correctly and incorrectly produced versions of the word “shoe”; Group 2 children listened to the words “shoe” and “moo”; Group 3 children listened to the words “cat” and “Pete.” A computer game was used to provide reinforcement for correct identification of the words. All children received the same traditional sound production training program for correction of their /∫/ error, concurrently with speech perception training, during six weekly treatment sessions. On post-testing, Group 1 and 2 children demonstrated a superior ability to articulate the target sound in comparison to Group 3 children. The results are interpreted in relation to previous research on this topic.

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Marwick, Alice E. "Morally Motivated Networked Harassment as Normative Reinforcement". Social Media + Society 7, n. 2 (aprile 2021): 205630512110213. http://dx.doi.org/10.1177/20563051211021378.

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While online harassment is recognized as a significant problem, most scholarship focuses on descriptions of harassment and its effects. We lack explanations of why people engage in online harassment beyond simple bias or dislike. This article puts forth an explanatory model where networked harassment on social media functions as a mechanism to enforce social order. Drawing from examples of networked harassment taken from qualitative interviews with people who have experienced harassment ( n = 28) and Trust & Safety workers at social platforms ( n = 9), the article builds on Brady, Crockett, and Bavel’s model of moral contagion to explore how moral outrage is used to justify networked harassment on social media. In morally motivated networked harassment, a member of a social network or online community accuses a target of violating their network’s norms, triggering moral outrage. Network members send harassing messages to the target, reinforcing their adherence to the norm and signaling network membership. Frequently, harassment results in the accused self-censoring and thus regulates speech on social media. Neither platforms nor legal regulations protect against this form of harassment. This model explains why people participate in networked harassment and suggests possible interventions to decrease its prevalence.

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Hartono, Dewi Kusuma. "Rehabilitation Management of Post Palatoplasty in Adolescent: Case Report". Surabaya Physical Medicine and Rehabilitation Journal 3, n. 2 (27 agosto 2021): 85. http://dx.doi.org/10.20473/spmrj.v3i2.28031.

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The objective of this study is to report comprehensive physical medicine and rehabilitation management of post-palatoplasty in an adolescent patient. The cleft palate repair is usually done in the first year of life in order to gain optimal speech function. To achieve normal communication development, normal hearing, normal oral and pharyngeal structures, adequate stimulation, and reinforcement from the environment for communication efforts are needed. Patient was assessed comprehensively using The International Classification of Functioning, Disability and Health (ICF) and by perceptual assessment for speech. The results showed that after one month intervention, the perceptions of hypernasality in plosive /b/ and affricates /c/ were found reduced significantly. There was improvement in articulation after one month of intervention.

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 Nagornyy, Igor A. "EXPRESSIVE RESOURCE OF INTERJECTIONS IN A LITERARY TEXT (BASED ON THE MATERIAL OF V. M. SHUKSHIN’S MOVIE-ESSAY “KALINA KRASNAYA”)". Culture and Text, n. 55 (2023): 146–61. http://dx.doi.org/10.37386/2305-4077-2023-4-146-161.

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The article analyzes the features of the functioning of interjections in the literary text as expressivizers of the reported, establishes the functional and communicative characteristics of interjections in the semantic space of the text dialogue, specifies the parameters of the reinforcement of the interjections of the represented individual reactions of the characters. It is proved that interjections in the communicative space of a literary text acquire the properties of significant speech signs manifesting the speech characteristics of characters in the modus-evaluative sphere of text dialogue. The functional and communicative-pragmatic properties of interjections as representatives of a character’s personal opinion and means of conveying this opinion to the addressee in a text dialogue are investigated.

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Devi, K. Akhilandeswari, K. Chinnanagaraju, V. Anil Santosh e D. D. D. Suribabu. "Intelligent Traffic Control System using Deep Reinforcement Learning". International Journal for Research in Applied Science and Engineering Technology 11, n. 8 (31 agosto 2023): 182–88. http://dx.doi.org/10.22214/ijraset.2023.55153.

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Abstract: Road Traffic signals are placed in particular positions to make sure the protection of vacationers. In this paper, we proposed a device for site visitors signal detection and recognition, in addition to a technique for extracting an avenue signal from an herbal complicated photo for processing and alerting the driving force via voice command. The reliability of the device is increased through different aspects such as noise, partial or absolute underexposure, partial or whole overexposure, considerable versions in shadeation saturation, extensive sort of viewing angles, view depth, and shape/shadeation deformations of site visitors symptoms etc. The proposed structure is sectioned into three phases. The first of that is photo pre-processing, where the dataset`s enter files are quantified, which decides the enter length for getting to know purposes, and resizes the records for the getting to know step. A Convolutional Neural Network (CNN) is used to train within the segment side which further more offers the text-to-speech translation, with the detected signal from the second one segment being supplied in audio format, which demonstrates better accuracy.

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Fakhrurroja, Hanif, Carmadi Machbub, Ary Setijadi Prihatmanto e Ayu Purwarianti. "Multimodal Fusion Algorithm and Reinforcement Learning-Based Dialog System in Human-Machine Interaction". International Journal on Electrical Engineering and Informatics 12, n. 4 (31 dicembre 2020): 1016–46. http://dx.doi.org/10.15676/ijeei.2020.12.4.19.

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Studies on human-machine interaction system show positive results on system development accuracy. However, there are problems, especially using certain input modalities such as speech, gesture, face detection, and skeleton tracking. These problems include how to design an interface system for a machine to contextualize the existing conversations. Other problems include activating the system using various modalities, right multimodal fusion methods, machine understanding of human intentions, and methods for developing knowledge. This study developed a method of human-machine interaction system. It involved several stages, including a multimodal activation system, methods for recognizing speech modalities, gestures, face detection and skeleton tracking, multimodal fusion strategies, understanding human intent and Indonesian dialogue systems, as well as machine knowledge development methods and the right response. The research contributes to an easier and more natural humanmachine interaction system using multimodal fusion-based systems. The average accuracy rate of multimodal activation, testing dialogue system using Indonesian, gesture recognition interaction, and multimodal fusion is 87.42%, 92.11%, 93.54% and 93%, respectively. The level of user satisfaction towards the multimodal recognition-based human-machine interaction system developed was 95%. According to 76.2% of users, this interaction system was natural, while 79.4% agreed that the machine responded well to their wishes.

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Cai, Zeyuan, Zhiquan Feng, Liran Zhou, Changsheng Ai, Haiyan Shao e Xiaohui Yang. "A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning". Computational Intelligence and Neuroscience 2022 (28 settembre 2022): 1–13. http://dx.doi.org/10.1155/2022/2341898.

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Despite the emergence of various human-robot collaboration frameworks, most are not sufficiently flexible to adapt to users with different habits. In this article, a Multimodal Reinforcement Learning Human-Robot Collaboration (MRLC) framework is proposed. It integrates reinforcement learning into human-robot collaboration and continuously adapts to the user's habits in the process of collaboration with the user to achieve the effect of human-robot cointegration. With the user's multimodal features as states, the MRLC framework collects the user's speech through natural language processing and employs it to determine the reward of the actions made by the robot. Our experiments demonstrate that the MRLC framework can adapt to the user's habits after repeated learning and better understand the user's intention compared to traditional solutions.

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Suzuki, Kenji. "AI: A New Open Access Journal for Artificial Intelligence". AI 1, n. 2 (26 marzo 2020): 141–42. http://dx.doi.org/10.3390/ai1020007.

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As a branch of computer science, artificial intelligence (AI) attempts to understand the essence of intelligence, and produce new kinds of intelligent machines that can respond in a similar way to human intelligence, with broad research areas of machine and deep learning, data science, reinforcement learning, data mining, knowledge discovery, knowledge reasoning, speech recognition, natural language processing, language recognition, image recognition, computer vision, planning, robotics, gaming, and so on [...]

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Fonseca, Melissa Gabriela Bravo, Heron Oliveira Hilário, Kurt Kotrschal, Friederike Range, Zsófia Virányi, Marina Henriques Lage Duarte, Laryssa Cristina Gomes Pereira e Angélica da Silva Vasconcellos. "The Power of Discourse: Associations between Trainers’ Speech and the Responses of Socialized Wolves and Dogs to Training". Animals 13, n. 6 (16 marzo 2023): 1071. http://dx.doi.org/10.3390/ani13061071.

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In a previous study, we found that Positive Reinforcement Training reduced cortisol of wolves and dogs; however, this effect varied across trainer–animal dyads. Here we investigate whether and how the trainers’ use of speech may contribute to this effect. Dogs’ great interest in high-pitched, intense speech (also known as Dog Directed Speech) has already been reported, but whether and how wolves respond similarly/differently to voice characteristics has never been studied before. We analyzed 270 training sessions, conducted by five trainers, with nine mixed-breed dogs and nine wolves, all human-socialized. Through Generalized Linear Mixed Models, we analyzed the effects of (a) three speech categories (nice, neutral, reprehensive) and laugh; and (b) acoustic characteristics of trainers’ voices on animals’ responses (correct responses, latency, orientation, time at less than 1 m, non-training behaviors, tail position/movements, cortisol variation). In both subspecies, tail wagging occurred more often in sessions with longer durations of nice speech, and less often in sessions with reprehensive speech. For dogs, the duration of reprehensive speech within a session was also negatively related to correct responses. For wolves, retreat time was associated with more reprehensive speech, whereas duration of nice speech was positively associated with time spent within one meter from the trainer. In addition, most dog behavioral responses were associated with higher average intonations within sessions, while wolf responses were correlated with lower intonations within sessions. We did not find any effects of the variables considered on cortisol variation. Our study highlights the relevance of voice tone and speech in a training context on animals’ performances and emotional reactions.

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