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Electrical Engineering and Systems Science > Audio and Speech Processing
Title: end-to-end speech recognition: a survey.
Abstract: In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning brought considerable reductions in word error rate of more than 50% relative, compared to modeling without deep learning. In the wake of this transition, a number of all-neural ASR architectures were introduced. These so-called end-to-end (E2E) models provide highly integrated, completely neural ASR models, which rely strongly on general machine learning knowledge, learn more consistently from data, while depending less on ASR domain-specific experience. The success and enthusiastic adoption of deep learning accompanied by more generic model architectures lead to E2E models now becoming the prominent ASR approach. The goal of this survey is to provide a taxonomy of E2E ASR models and corresponding improvements, and to discuss their properties and their relation to the classical hidden Markov model (HMM) based ASR architecture. All relevant aspects of E2E ASR are covered in this work: modeling, training, decoding, and external language model integration, accompanied by discussions of performance and deployment opportunities, as well as an outlook into potential future developments.
Comments: | Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Subjects: | Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD) |
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- DOI: 10.1007/s11042-024-19848-8
- Corpus ID: 271340568
Loss of fingerprint features and recognition failure due to physiological factors- a literature survey
- Imran Riaz , Ahmad Nazri Ali , Haidi Ibrahim
- Published in Multimedia tools and… 19 July 2024
55 References
Deep learning-driven palmprint and finger knuckle pattern-based multimodal person recognition system, hand-based multibiometric systems: state-of-the-art and future challenges, an intelligent system for automatic fingerprint identification using feature fusion by gabor filter and deep learning, effects of oils, lotions, hand sanitizers, and mehendi on fingerprints captured through digital fingerprint scanner, fingerprint classification based on deep learning approaches: experimental findings and comparisons, the impact of time, environmental conditions, and sex on the degradation of bloody fingerprints on a glass surface, a survey of iris datasets, feature extraction, recognition, and matching of damaged fingerprint: application of deep learning network, fingerprint indexing for wrinkled fingertips immersed in liquids, a comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities, related papers.
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Trends in speech emotion recognition: a comprehensive survey
- Published: 22 February 2023
- Volume 82 , pages 29307–29351, ( 2023 )
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- Kamaldeep Kaur ORCID: orcid.org/0000-0002-3542-1214 1 , 2 &
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Among the other modes of communication, such as text, body language, facial expressions, and so on, human beings employ speech as the most common. It contains a great deal of information, including the speaker’s feelings. Detecting the speaker’s emotions from his or her speech has shown to be quite useful in a variety of real-world applications. The dataset development, feature extraction, feature selection/dimensionality reduction, and classification are the four primary processes in the Speech Emotion Recognition process. In this context, more than 70 studies are thoroughly examined in terms of their databases, emotions, features extracted, and classifiers employed. The databases, characteristics, extraction and classification methods, as well as the results, are all thoroughly examined. The study also includes a comparative analysis of these research papers.
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Automatic Speech Emotion Recognition: a Systematic Literature Review
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We would like to thank IKG Punjab Technical University, Kapurthala, Punjab (India) for providing the opportunity to carry out the research work.
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Kamaldeep Kaur
Department of Computer Science & Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
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Kaur, K., Singh, P. Trends in speech emotion recognition: a comprehensive survey. Multimed Tools Appl 82 , 29307–29351 (2023). https://doi.org/10.1007/s11042-023-14656-y
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Received : 15 December 2021
Revised : 06 April 2022
Accepted : 03 February 2023
Published : 22 February 2023
Issue Date : August 2023
DOI : https://doi.org/10.1007/s11042-023-14656-y
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A huge amount of research has been done in the field of speech signal processing in recent years. In particular, there has been increasing interest in the automatic speech recognition (ASR) technology field. ASR began with simple systems that responded to a limited number of sounds and has evolved into sophisticated systems that respond fluently to natural language. This systematic review of ...
Specifically, this literature survey gives a concise introduction to ASR and provides an overview of the general architectures dealing with speaker recognition technologies, and upholds the past ...
Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods. This study explores different feature extraction methods, state-of-the-art classification ...
1.2 Our contribution. In this section, we will discuss how our research work is different from the earlier SR surveys. Most of the recent surveys have targeted specific areas in speech recognition like ASR survey on [], Acoustic-Phonetic Analysis [], Mandarin speech recognition [], Speech emotion recognition [44, 45], speech denoising methods [], ML and DL Applications [], state-of-the-art ...
ASR can be defined as the process of deriving the. transcription of speech, known as a word sequence, in which. the focus is on the shape of the speech wave [1]. In actuality, speech recognition ...
The speech-emotion recognition (SER) field became crucial in advanced Human-computer interaction (HCI). ... It shows that most previous survey papers are literature reviews and a few attempts to do a systematic literature review for SER, and all covered the main parts of SER. However, there are some limitations in covering the preprocess ...
Abstract. Nowadays emotion recognition from speech (SER) is a demanding research area for researchers because of its wide real-life applications. There are many challenges for SER systems such as the availability of suitable emotional databases, identification of the relevant feature vector, and suitable classifiers.
The literature on noisy speech emotion recognition has identified three main approaches to processing noisy speech emotion data for classification or regression analysis. The first is speech enhancement pre-processing to improve or remove noise from raw speech signals or calculated features.
Automatic Speech Emotion Recognition (ASER) has recently garnered attention across various fields including artificial intelligence, pattern recognition, and human-computer interaction. However, ASER encounters numerous challenges such as a shortage of diverse datasets, appropriate feature selection, and suitable intelligent recognition techniques. To address these challenges, a systematic ...
Title: End-to-End Speech Recognition: A Survey Authors: Rohit Prabhavalkar , Takaaki Hori , Tara N. Sainath , Ralf Schlüter , Shinji Watanabe View a PDF of the paper titled End-to-End Speech Recognition: A Survey, by Rohit Prabhavalkar and 4 other authors
This literature survey gives a concise introduction to ASR and provides an overview of the general architectures dealing with speaker recognition technologies, and upholds the past, present, and future research trends in this area. Humans can identify a speaker by listening to their voice, over the telephone, or on any digital devices. Acquiring this congenital human competency, authentication ...
techniques within the domain of Speech Recognition through a systematic literature review of the existing work. We will introduce the most significant and relevant techniques that may provide some directions in the future research. Key words: Automatic Speech Recognition (ASR), End-to-End Systems, Hybrid Systems, Low Resource Language.
work is a systematic review of the literature on V oice assistants (V A). Innovative mode of interaction, the V oice. Assistant definition is derived from advances in artificial intelligence ...
This paper provides a comprehensive survey on voice recognition, its applications, voice recognition methods and provides a systematic review of the existing literature based various aspects. Published in: 2022 International Mobile and Embedded Technology Conference (MECON) Article #: Date of Conference: 10-11 March 2022.
This paper analysis the accuracy of feature extraction based on modeling which is implemented using MFCC and HMM for two different type connected and continuous speech. The recognition result shows that the overall system accuracy for connected word is 69.22 % and continuous word is 50%.
During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker's existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and ...
visual speech recognition technology that recognizes the speech content based on the motion characteristics of the speaker's lips without speech signals. Therefore, lipreading can detect the speaker's content in a noisy environment, even without a voice signal. This article summarizes the main research from traditional methods to
Speech emotion recognition methods: A literature review. January 2017; AIP Conference Proceedings 1891(020105):020105-1 - 020105-7 ... This paper is a survey of speech emotion classification ...
From many decades, people are working research in the area of voice recognition and communication. Researchers have valuable contribution in this area up to now. They have also contributed in the field of voice recognition as well as speaker dependent and speaker independent voice recognition. We have studied researcher's contribution and tried to provide a survey of scientific and an ...
In addition, there may be more than one feeling in a speech and a combination of different emotions at the moment. In contrast, the determination of the boundary and speech of emotion recognition is even complicated by human factors [].On the other hand, the feeling of speech in the language depends on the culture and language, the internal states, the content of the speech, the gender, the ...
Ghai and Singh (2012) and Kurian (2014) have presented a survey on speech recognition over the year 2000-2013 for various Indian languages i.e. Gujarati, Oriya, Punjabi, etc. Approving the Thakur ...
A simple rule-based part of speech tagger. In Speech and Natural ... Named entity recognition: A literature survey. Center For Indian Language Technology, pages 1-27, 2014 ... Nested named entity recognition: A survey, ACM Transactions on Knowledge Discovery from Data (TKDD) 16 (6) (2022) 1-29. Google Scholar [43] Yan Wen, Cong Fan, Geng ...
DOI: 10.1007/s11042-024-19848-8 Corpus ID: 271340568; Loss of fingerprint features and recognition failure due to physiological factors- a literature survey @article{Riaz2024LossOF, title={Loss of fingerprint features and recognition failure due to physiological factors- a literature survey}, author={Imran Riaz and Ahmad Nazri Ali and Haidi Ibrahim}, journal={Multimedia Tools and Applications ...
Among the other modes of communication, such as text, body language, facial expressions, and so on, human beings employ speech as the most common. It contains a great deal of information, including the speaker's feelings. Detecting the speaker's emotions from his or her speech has shown to be quite useful in a variety of real-world applications. The dataset development, feature extraction ...
Furthermore, a survey conducted by El et al. [25] addressed three important aspects of designing a speech recognition system: the choice of suitable features for speech recognition, the design of ...