Relevant bibliographies by topics / Speech reinforcement

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Speech reinforcement'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Speech reinforcement.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Speech reinforcement"

1

Crespo, Joao B., and Richard C. Hendriks. "Multizone Speech Reinforcement." IEEE/ACM Transactions on Audio, Speech, and Language Processing 22, no. 1 (2014): 54–66. http://dx.doi.org/10.1109/tasl.2013.2283100.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gibson, Jerry, and Hoontaek Oh. "A Reinforcement Learning Approach to Speech Coding." Information 13, no. 7 (2022): 331. http://dx.doi.org/10.3390/info13070331.

Full text
Abstract:
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 proc
APA, Harvard, Vancouver, ISO, and other styles
3

Mardhatillah, Elsy. "Teacher’s Reinforcement in English Classroom in MTSS Darul Makmur Sungai Cubadak." Indonesian Research Journal On Education 3, no. 1 (2022): 825–32. http://dx.doi.org/10.31004/irje.v3i1.202.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
4

Ravikiran, Pichika, and Midhun Chakkaravarthy. "Enhancing Speech-to-Text Conversion with Convolutional Reinforcement Learning Algorithms." International Journal of Science and Research (IJSR) 13, no. 8 (2024): 1118–22. http://dx.doi.org/10.21275/sr24515225027.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

CHOI, Jae-Hun, Joon-Hyuk CHANG, and 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, no. 9 (2010): 1684–87. http://dx.doi.org/10.1587/transfun.e93.a.1684.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Alkaher, Yehav, and Israel Cohen. "Temporal Howling Detector for Speech Reinforcement Systems." Acoustics 4, no. 4 (2022): 967–95. http://dx.doi.org/10.3390/acoustics4040060.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
7

Ortega, A., E. Lleida, and E. Masgrau. "Speech reinforcement system for car cabin communications." IEEE Transactions on Speech and Audio Processing 13, no. 5 (2005): 917–29. http://dx.doi.org/10.1109/tsa.2005.853006.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Czyzewski, Andrzej. "Optimizing medical personnel speech recognition models using speech synthesis and reinforcement learning." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A202—A203. http://dx.doi.org/10.1121/10.0023271.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
10

Isaac, Samson, Khalid Haruna, Muhammad Aminu Ahmad, and Rabi Mustapha. "DEEP REINFORCEMENT LEARNING WITH HIDDEN MARKOV MODEL FOR SPEECH RECOGNITION." JOURNAL OF TECHNOLOGY & INNOVATION 3, no. 1 (2023): 01–05. http://dx.doi.org/10.26480/jtin.01.2023.01.05.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Speech reinforcement"

1

McMinn, Terrance. "Development Of An Evaluation Tool For Use At The Design Stage Of Auditoria With Respect To Unassisted Speech Reinforcement." Thesis, Curtin University, 1996. http://hdl.handle.net/20.500.11937/1639.

Full text
Abstract:
This dissertation describes the development of an evaluation tool that can be used by an acoustican during the design stage of enclosures used for unassisted speech. Enclosures include lecture theatres, lecture halls and speech auditoriums. The tool is designed to enable Acousticians to be able to manipulate various acoustical parameters such as the geometry and the materials or construction selection to gauge the impact on speech performance. The tool can also be used to evaluate the performance of speech privacy within spaces using the Speech Transmission Index. Computer simulation tools hav
APA, Harvard, Vancouver, ISO, and other styles
2

McMinn, Terrance. "Development Of An Evaluation Tool For Use At The Design Stage Of Auditoria With Respect To Unassisted Speech Reinforcement." Curtin University of Technology, School of Architecture, Construction and Planning, 1996. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=12331.

Full text
Abstract:
This dissertation describes the development of an evaluation tool that can be used by an acoustican during the design stage of enclosures used for unassisted speech. Enclosures include lecture theatres, lecture halls and speech auditoriums. The tool is designed to enable Acousticians to be able to manipulate various acoustical parameters such as the geometry and the materials or construction selection to gauge the impact on speech performance. The tool can also be used to evaluate the performance of speech privacy within spaces using the Speech Transmission Index. Computer simulation tools hav
APA, Harvard, Vancouver, ISO, and other styles
3

Saavedra, Ingrid Marcela. "Free Operant Comparison of Interventions for Problematic Speech Using Reinforcement With and Without Preferred Topics." Scholarly Commons, 2019. https://scholarlycommons.pacific.edu/uop_etds/3608.

Full text
Abstract:
Deficits in conversation skills can be one barrier to developing and maintaining relationships for individuals with autism spectrum disorder (ASD). Individuals with ASD may deter conversation partners if they do not stay on topic or if they dwell on topics. Several interventions have been identified in targeting the reduction of problematic (off-topic or perseverative) speech, and withheld attention for its occurrence. In addition to leveraging attention as a reinforcer, one study provided signaled access to preferred topics contingent on talking about non-perseverative or therapist-selected t
APA, Harvard, Vancouver, ISO, and other styles
4

Nalamothu, Abhishek. "Abusive and Hate Speech Tweets Detection with Text Generation." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567510940365305.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kim, Hanna Y. "The use of differential reinforcement of other behavior (DRO) to reduce scripting in a child with autism." Thesis, Kaplan University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1539953.

Full text
Abstract:
<p> This case study evaluated the effects of differential reinforcement of other behavior (DRO) on scripting in a four year-old child with Autism Spectrum Disorder, Obsessive Compulsive Disorder and Celiac Disease. The overall goal was to show that DRO as the only independent variable could reduce scripting in a child with autism. A vibrator was set to vibrate every six minutes to indicate the end of each interval during intervention and the behavior was measured using a partial-interval time sampling method during the two hour in-home private Applied Behavior Analysis session over a two month
APA, Harvard, Vancouver, ISO, and other styles
6

Acevedo, Valle Juan Manuel. "Sensorimotor exploration: constraint awareness and social reinforcement in early vocal development." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/667500.

Full text
Abstract:
This research is motivated by the benefits that knowledge regarding early development in infants may provide to different fields of science. In particular, early sensorimotor exploration behaviors are studied in the framework of developmental robotics. The main objective is about understanding the role of motor constraint awareness and imitative behaviors during sensorimotor exploration. Particular emphasis is placed on prelinguistic vocal development because during this stage infants start to master the motor systems that will later allow them to pronounce their first words. Previous works
APA, Harvard, Vancouver, ISO, and other styles
7

Budhan, Jamie A. "The Impact of a Novel Gaming Reinforcement System on Oral Intake Outcomes in Pediatric Dysphagia Therapy: A Pilot Study." Miami University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=miami1525427023914417.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lee, Joanna Chen. "Are individual differences in language associated with differences in the corticostriatal system? A behavioral and imaging study." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2927.

Full text
Abstract:
The overall aim of the current research was to investigate the corticostriatal system in developmental language impairment (DLI) at the behavioral and neuroanatomical levels. Two groups of young adults, one with DLI (N = 25) and the other without (N = 23), participated in the behavioral study. A sample of procedural learning and reinforcement learning (RL) tasks was selected. Each task represents a unique aspect of procedural memory, and learning processes during these tasks have been linked, at least partially, to the functionality o
APA, Harvard, Vancouver, ISO, and other styles
9

Gentet, Enguerrand. "Amélioration de l'intelligibilité de signaux audio de parole en contexte bruité automobile." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT008.

Full text
Abstract:
La quantité de diffusion de signaux de parole dans les habitacles automobiles est de plus en plus importante : télécommunications, radio, système de navigation... Cependant, malgré les efforts et les avancées mécaniques, beaucoup de bruits persistent au sein de l'habitacle dégradant fortement l'intelligibilité de ces signaux de parole. L'objectif de cette thèse est alors de développer des outils de renforcement de la parole visant à traiter les signaux avant leur dégradation afin d'assurer une bonne intelligibilité dans le bruit des habitacles automobiles. Une approche de renforcement de la pa
APA, Harvard, Vancouver, ISO, and other styles
10

Dabare, Gamage Hasitha Dilshani. "Adaptive driving-speed control at signalised intersection using reinforcement learning." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121732/1/Hasitha%20Dilshani_Dabare%20Gamage_Thesis.pdf.

Full text
Abstract:
Vehicles driving in the urban settings experience substantial disturbances from neighbouring vehicles and traffic signals. Deviating from the optimal trajectory causes excessive fuel consumption and delay. This research proposes a novel adaptive driving-speed-control algorithm using a Reinforcement Learning (Q-learning) approach. The proposed algorithm can respond to the prevailing traffic conditions and traffic-controls conditions at signalised intersection environment and provides the control vehicle with target driving-speeds to achieve fuel savings. The micro-simulation results confirm the
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Speech reinforcement"

1

Lin, Baihan. Reinforcement Learning Methods in Speech and Language Technology. Springer Nature Switzerland, 2025. http://dx.doi.org/10.1007/978-3-031-53720-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Eby, Carly Moher. Effects of Social Reinforcement Versus Tokens on the Spontaneous Speech of Preschoolers. [publisher not identified], 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Rieser, Verena. Bootstrapping reinforcement learning-based dialogue strategies from wizard-of-oz data. German Research Center for Artificial Intelligence, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Rieser, Verena. Bootstrapping reinforcement learning-based dialogue strategies from wizard-of-oz data. German Research Center for Artificial Intelligence, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Lin, Baihan. Reinforcement Learning Methods in Speech and Language Technology. Springer, 2024.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Speech reinforcement"

1

Mapp, Peter. "Speech Intelligibility of Sound Systems." In Sound Reinforcement for Audio Engineers. Focal Press, 2022. http://dx.doi.org/10.4324/9781003220268-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Eargle, John. "Loudspeakers in Speech and Music Reinforcement." In Loudspeaker Handbook. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-5678-4_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Eargle, John M. "Principles of Speech and Music Reinforcement." In Music, Sound, and Technology. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-5936-5_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Eargle, John M. "Principles of Speech and Music Reinforcement." In Music, Sound, and Technology. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-011-7070-3_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kamath, Uday, John Liu, and James Whitaker. "Deep Reinforcement Learning for Text and Speech." In Deep Learning for NLP and Speech Recognition. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14596-5_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Maragoudakis, Manolis, Todor Ganchev, and Nikos Fakotakis. "Bayesian Reinforcement for a Probabilistic Neural Net Part-of-Speech Tagger." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30120-2_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Lin, Baihan. "Reinforcement Learning Formulations for Speech and Language Applications." In Signals and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53720-2_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ribeiro, Ricardo, and David Martins de Matos. "Summarizing Speech by Contextual Reinforcement of Important Passages." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28885-2_44.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, Jianrong, Xiaomin Li, Xuewei Li, Mei Yu, Qiang Fang, and Li Liu. "MVNet: Memory Assistance and Vocal Reinforcement Network for Speech Enhancement." In Neural Information Processing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30108-7_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Lin, Baihan. "Reinforcement Learning in Automatic Speech Recognition (ASR): The Voice-First Revolution." In Signals and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53720-2_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Speech reinforcement"

1

Nagpal, Chirag, Subhashini Venugopalan, Jimmy Tobin, Marilyn Ladewig, Katherine Heller, and Katrin Tomanek. "Speech Recognition with LLMs Adapted to Disordered Speech Using Reinforcement Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888006.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kim, Gyu Seon, Samuel Yen-Chi Chen, Soohyun Park, and Joongheon Kim. "Quantum Reinforcement Learning for Coordinated Satellite Systems." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889145.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Guo, Haihong, Fengxin Li, Jiao Li, and Hongyan Liu. "KAN v.s. MLP for Offline Reinforcement Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888327.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Liang, Zhenhua, Xueqiong Li, Jun-Jie Huang, Nan Hu, Shaowu Yang, and Hengzhu Liu. "Multi-layer Network Disintegration via Deep Reinforcement Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888653.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Bai, He, Hui Li, Jianming Que, et al. "MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888362.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Guo, Haihong, Fengxin Li, Jiao Li, and Hongyan Liu. "Offline Reinforcement Learning via Conservative Smoothing and Dynamics Controlling." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889801.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Jie, Yirong Yao, Wei He, Yiqun Niu, and Chongjun Wang. "Regret Optimization Experience Replay in Off-Policy Reinforcement Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889888.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yang, Jing, Jin Yang, Chenwei Wu, et al. "Multiple Sclerosis Detection with Reinforcement Learning and Differential Evolution." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889136.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Li, Haorui, Jiaqi Liang, Linjing Li, and Daniel Zeng. "Conservative Offline Meta-Reinforcement Learning with Task Similarity Measurement." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888196.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Li, Tongyue, Dianxi Shi, Songchang Jin, Zhen Wang, Huanhuan Yang, and Yang Chen. "Multi-Agent Hierarchical Graph Attention Actor-Critic Reinforcement Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888861.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Speech reinforcement"

1

Liu, Tairan. Addressing Urban Traffic Congestion: A Deep Reinforcement Learning-Based Approach. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2322.

Full text
Abstract:
In an innovative venture, the research team embarked on a mission to redefine urban traffic flow by introducing an automated way to manage traffic light timings. This project integrates two critical technologies, Deep Q-Networks (DQN) and Auto-encoders, into reinforcement learning, with the goal of making traffic smoother and reducing the all-too-common road congestion in simulated city environments. Deep Q-Networks (DQN) are a form of reinforcement learning algorithms that learns the best actions to take in various situations through trial and error. Auto-encoders, on the other hand, are tool
APA, Harvard, Vancouver, ISO, and other styles
2

WIND RESISTANCE PERFORMANCE AND REINFORCEMENT MEASURES OF STANDING SEAM METAL ROOFS BASED ON WIND TUNNEL TESTS. The Hong Kong Institute of Steel Construction, 2025. https://doi.org/10.18057/ijasc.2025.21.1.1.

Full text
Abstract:
As an important building envelope structure, metal roofs have been widely used in various large-scale buildings because of their unique advantages such as beautiful shape, lightweight nature, flexibility, and exceptional strength. However, there are still some gaps in the existing research on the wind resistance performance of metal roof systems, which poses a serious threat to life and property. To address this gap, this paper conducted wind tunnel tests of long-span standing seam metal roofs. The basic wind pressure of 0.95 kN/m2, which is once every 100 years. The wind-induced response of t
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Speech reinforcement' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography