Multilingual spoken term detection: a review

Deekshitha, G; Mary, Leena

doi:10.1007/s10772-020-09732-9

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…𝑃 = (𝑁 𝑡𝑝 (𝑁 𝑡𝑝 + 𝑁 𝑓𝑝 ) ⁄ ) × 100 (6) In the equations, Ntp, Nfp, Nfn, and Ntotal referred to the number of true positives, false positives, false negatives, and total samples in all the segments respectively. Furthermore, the performance was evaluated using area under curve (AUC) and detection error trade-off (DET) curve.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

“…One of the popular techniques was Spoken Term searching technique, which could be further divided into Spoken Term Detection (STD) and Keyword Spotting (KWS). Spoken Term Detection (STD), or spoken term discovery, is the identification of recurrent speech fragments from raw speech without prior knowledge of the language, i.e., automatic retrieval of speech from a database through specific audio keywords or queries [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is only reasonable for the detection task to be even more challenging when more than one individual contributed to the utterances of words in the speech database due to the differences in human vocal attributes, accent, dialect, gender, age and so on. This poses challenges in STD and speech recognition task as a whole [6,8].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Detection of Inappropriate Speech Content for Film Censorship

et al. 2022

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Audible content has become an effective tool for shaping one's personality and character due to the ease of accessibility to a huge audible content that could be an independent audio files or an audio of online videos, movies, and television programs. There is a huge necessity to filter inappropriate audible content of the easily accessible videos and films that are likely to contain an inappropriate speech content. With this in view, all the broadcasting and online video/audio platform companies hire a lot of manpower to detect the foul voices prior to censorship. The process has a large cost in terms of manpower, time and financial resources. In addition to inaccurate detection of foul voices due to fatigue of manpower and weakness of human visual and hearing system in long time and monotonous tasks. As such, this paper proposes an intelligent deep learning-based system for film censorship through a fast and accurate detection and localization approach using advanced deep Convolutional Neural Networks (CNNs). The dataset of foul language containing isolated words samples and continuous speech were collected, annotated, processed, and analyzed for the development of automated detection of inappropriate speech content. The results indicated the feasibility of the suggested systems by reporting a high volume of inappropriate spoken terms detection. The proposed system outperformed state-of-the-art baseline algorithms on the novel foul language dataset evaluation metrics in terms of macro average AUC (93.85%), weighted average AUC (94.58%), and all other metrics such as F1-score. Additionally, proposed acoustic system outperformed ASR-based system for profanity detection based on the evaluation metrics including AUC, accuracy, precision, and F1-score. Additionally, proposed system was proven to be faster than human manual screening and detection of audible content for films' censorship.

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Section: B Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Detection of Inappropriate Speech Content for Film Censorship

et al. 2022

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“…In particular, when applied to large collections with multilingual content, where the recordings cover a wide time span, such as found in OH collections, ASR proves to be a difficult task. A paradigm called transfer learning is a potential solution to training complex ASR models for under-resourced languages [25]. In this approach, a model trained on a well-resourced language serves as a basis.…”

Section: Automatic Speech Recognitionmentioning

confidence: 99%

A Computational Look at Oral History Archives

Pessanha

Salah

2021

J. Comput. Cult. Herit.

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Computational technologies have revolutionized the archival sciences field, prompting new approaches to process the extensive data in these collections. Automatic speech recognition and natural language processing create unique possibilities for analysis of oral history (OH) interviews, where otherwise the transcription and analysis of the full recording would be too time consuming. However, many oral historians note the loss of aural information when converting the speech into text, pointing out the relevance of subjective cues for a full understanding of the interviewee narrative. In this article, we explore various computational technologies for social signal processing and their potential application space in OH archives, as well as neighboring domains where qualitative studies is a frequently used method. We also highlight the latest developments in key technologies for multimedia archiving practices such as natural language processing and automatic speech recognition. We discuss the analysis of both visual (body language and facial expressions), and non-visual cues (paralinguistics, breathing, and heart rate), stating the specific challenges introduced by the characteristics of OH collections. We argue that applying social signal processing to OH archives will have a wider influence than solely OH practices, bringing benefits for various fields from humanities to computer sciences, as well as to archival sciences. Looking at human emotions and somatic reactions on extensive interview collections would give scholars from multiple fields the opportunity to focus on feelings, mood, culture, and subjective experiences expressed in these interviews on a larger scale.

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Low-Resource Speech Keyword Search Based on Residual Neural Network

Wang

Huang

et al. 2022

Lecture Notes in Computer Science

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Multilingual spoken term detection: a review

Cited by 7 publications

References 41 publications

Deep Learning-Based Detection of Inappropriate Speech Content for Film Censorship

Deep Learning-Based Detection of Inappropriate Speech Content for Film Censorship

A Computational Look at Oral History Archives

Low-Resource Speech Keyword Search Based on Residual Neural Network

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