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Relevant bibliographies by topics / Speech diarization

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Journal articles

Academic literature on the topic 'Speech diarization'

Author: Grafiati

Published: 30 November 2024

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Journal articles on the topic "Speech diarization"

1

Mertens, Robert, Po-Sen Huang, Luke Gottlieb, Gerald Friedland, Ajay Divakaran, and Mark Hasegawa-Johnson. "On the Applicability of Speaker Diarization to Audio Indexing of Non-Speech and Mixed Non-Speech/Speech Video Soundtracks." International Journal of Multimedia Data Engineering and Management 3, no. 3 (2012): 1–19. http://dx.doi.org/10.4018/jmdem.2012070101.

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A video’s soundtrack is usually highly correlated to its content. Hence, audio-based techniques have recently emerged as a means for video concept detection complementary to visual analysis. Most state-of-the-art approaches rely on manual definition of predefined sound concepts such as “ngine sounds,” “utdoor/indoor sounds.” These approaches come with three major drawbacks: manual definitions do not scale as they are highly domain-dependent, manual definitions are highly subjective with respect to annotators and a large part of the audio content is omitted since the predefined concepts are usu

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Astapov, Sergei, Aleksei Gusev, Marina Volkova, et al. "Application of Fusion of Various Spontaneous Speech Analytics Methods for Improving Far-Field Neural-Based Diarization." Mathematics 9, no. 23 (2021): 2998. http://dx.doi.org/10.3390/math9232998.

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Recently developed methods in spontaneous speech analytics require the use of speaker separation based on audio data, referred to as diarization. It is applied to widespread use cases, such as meeting transcription based on recordings from distant microphones and the extraction of the target speaker’s voice profiles from noisy audio. However, speech recognition and analysis can be hindered by background and point-source noise, overlapping speech, and reverberation, which all affect diarization quality in conjunction with each other. To compensate for the impact of these factors, there are a va

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3

Lyu, Ke-Ming, Ren-yuan Lyu, and Hsien-Tsung Chang. "Real-time multilingual speech recognition and speaker diarization system based on Whisper segmentation." PeerJ Computer Science 10 (March 29, 2024): e1973. http://dx.doi.org/10.7717/peerj-cs.1973.

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This research presents the development of a cutting-edge real-time multilingual speech recognition and speaker diarization system that leverages OpenAI’s Whisper model. The system specifically addresses the challenges of automatic speech recognition (ASR) and speaker diarization (SD) in dynamic, multispeaker environments, with a focus on accurately processing Mandarin speech with Taiwanese accents and managing frequent speaker switches. Traditional speech recognition systems often fall short in such complex multilingual and multispeaker contexts, particularly in SD. This study, therefore, inte

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Prabhala, Jagat Chaitanya, Venkatnareshbabu K, and Ragoju Ravi. "OPTIMIZING SIMILARITY THRESHOLD FOR ABSTRACT SIMILARITY METRIC IN SPEECH DIARIZATION SYSTEMS: A MATHEMATICAL FORMULATION." Applied Mathematics and Sciences An International Journal (MathSJ) 10, no. 1/2 (2023): 1–10. http://dx.doi.org/10.5121/mathsj.2023.10201.

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Speaker diarization is a critical task in speech processing that aims to identify "who spoke when?" in an audio or video recording that contains unknown amounts of speech from unknown speakers and unknown number of speakers. Diarization has numerous applications in speech recognition, speaker identification, and automatic captioning. Supervised and unsupervised algorithms are used to address speaker diarization problems, but providing exhaustive labeling for the training dataset can become costly in supervised learning, while accuracy can be compromised when using unsupervised approaches. This

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5

V, Sethuram, Ande Prasad, and R. Rajeswara Rao. "Metaheuristic adapted convolutional neural network for Telugu speaker diarization." Intelligent Decision Technologies 15, no. 4 (2022): 561–77. http://dx.doi.org/10.3233/idt-211005.

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In speech technology, a pivotal role is being played by the Speaker diarization mechanism. In general, speaker diarization is the mechanism of partitioning the input audio stream into homogeneous segments based on the identity of the speakers. The automatic transcription readability can be improved with the speaker diarization as it is good in recognizing the audio stream into the speaker turn and often provides the true speaker identity. In this research work, a novel speaker diarization approach is introduced under three major phases: Feature Extraction, Speech Activity Detection (SAD), and

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6

Murali, Abhejay, Satwik Dutta, Meena Chandra Shekar, Dwight Irvin, Jay Buzhardt, and John H. Hansen. "Towards developing speaker diarization for parent-child interactions." Journal of the Acoustical Society of America 152, no. 4 (2022): A61. http://dx.doi.org/10.1121/10.0015551.

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Daily interactions of children with their parents are crucial for spoken language skills and overall development. Capturing such interactions can help to provide meaningful feedback to parents as well as practitioners. Naturalistic audio capture and developing further speech processing pipeline for parent-child interactions is a challenging problem. One of the first important steps in the speech processing pipeline is Speaker Diarization—to identify who spoke when. Speaker Diarization is the method of separating a captured audio stream into analogous segments that are differentiated by the spe

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7

Taha, Thaer Mufeed, Zaineb Ben Messaoud, and Mondher Frikha. "Convolutional Neural Network Architectures for Gender, Emotional Detection from Speech and Speaker Diarization." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 03 (2024): 88–103. http://dx.doi.org/10.3991/ijim.v18i03.43013.

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This paper introduces three system architectures for speaker identification that aim to overcome the limitations of diarization and voice-based biometric systems. Diarization systems utilize unsupervised algorithms to segment audio data based on the time boundaries of utterances, but they do not distinguish individual speakers. On the other hand, voice-based biometric systems can only identify individuals in recordings with a single speaker. Identifying speakers in recordings of natural conversations can be challenging, especially when emotional shifts can alter voice characteristics, making g

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Kothalkar, Prasanna V., John H. L. Hansen, Dwight Irvin, and Jay Buzhardt. "Child-adult speech diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model and automatic speech recognition-based re-segmentation." Journal of the Acoustical Society of America 155, no. 2 (2024): 1198–215. http://dx.doi.org/10.1121/10.0024353.

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Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to

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9

Kshirod, Kshirod Sarmah. "Speaker Diarization with Deep Learning Techniques." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (2020): 2570–82. http://dx.doi.org/10.61841/turcomat.v11i3.14309.

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Speaker diarization is a task to identify the speaker when different speakers spoke in an audio or video recording environment. Artificial intelligence (AI) fields have effectively used Deep Learning (DL) to solve a variety of real-world application challenges. With effective applications in a wide range of subdomains, such as natural language processing, image processing, computer vision, speech and speaker recognition, and emotion recognition, cyber security, and many others, DL, a very innovative field of Machine Learning (ML), that is quickly emerging as the most potent machine learning te

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Lleida, Eduardo, Alfonso Ortega, Antonio Miguel, et al. "Albayzin 2018 Evaluation: The IberSpeech-RTVE Challenge on Speech Technologies for Spanish Broadcast Media." Applied Sciences 9, no. 24 (2019): 5412. http://dx.doi.org/10.3390/app9245412.

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The IberSpeech-RTVE Challenge presented at IberSpeech 2018 is a new Albayzin evaluation series supported by the Spanish Thematic Network on Speech Technologies (Red Temática en Tecnologías del Habla (RTTH)). That series was focused on speech-to-text transcription, speaker diarization, and multimodal diarization of television programs. For this purpose, the Corporacion Radio Television Española (RTVE), the main public service broadcaster in Spain, and the RTVE Chair at the University of Zaragoza made more than 500 h of broadcast content and subtitles available for scientists. The dataset includ

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