Automatic Subtitle Generation for Videos

Ramani, A.; Rao, Asmita; Vidya, V.; Prasad, Vishnu

doi:10.1109/icaccs48705.2020.9074180

Cited by 12 publications

(4 citation statements)

References 2 publications

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“…Aditya focus on Automatic Subtitle Generation for videos [3] and [13]. The aim is to overcome issues in incorporating subtitles into videos, generally downloaded to third party source and adding manually to media player which requires quality internet and readily available subtitles.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Caption Generation for Video Call with Language Translation

Polepaka,

Prashanth Kumar,

Umesh Chandra

et al. 2023

E3S Web Conf.

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In the modern era, virtual communication between individuals is common. Many people’s lives have been made simpler in a number of circumstances by providing subtitles, generating automated captions for social media videos, and language translation from a source language to a targeted language. Both are included, which offers face-to-face translated captions during video conversations. React is used for application development. To send the data, socket programming is utilized. Context is understood and translated using Google translate API and speech recognition modules. With OpenAI and Whisper, captions are generated. This paper will directly create the translated user voice rather than translating the text and creating subtitles.

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Section: Literature Surveymentioning

confidence: 99%

Automated Caption Generation for Video Call with Language Translation

Polepaka,

Prashanth Kumar,

Umesh Chandra

et al. 2023

E3S Web Conf.

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“…The literature emphasizes the significance of large vocabulary speech recognition systems, with open-source options like the Sphinx toolkit standing out (Gaida, et al, 2014). Comparative studies between speech recognition engines, such as CMUSphinx and Mozilla DeepSpeech, highlight the importance of performance and resource efficiency (Ramani, Rao, Vidya, & Prasad, 2020). Research also explores advancements in decoder algorithms, including recurrent neural network (RNN) encoder-decoder approaches for continuous large vocabulary speech recognition (Ramani, Rao, Vidya, & Prasad, 2020) (Toshniwal, et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Subtitle Generator for Sinhala Speech

R.V.P.S. Akesh,

R.G.N. Meegama

2023

VJScience

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In today’s digital era, the significance of speech recognition technology cannot be overstated as it plays a pivotal role in enabling human-computer interaction and supporting various applications. This paper focuses on the development of a real-time subtitle generator for Sinhala speech using speech recognition techniques. The CMUSphinx toolkit, an open-source toolkit based on the Hidden Markov Model (HMM), is employed for the implementation of the application. Mel-frequency cepstral coefficients (MFCC) are utilized for feature extraction from the given ’wav’ format recordings. The paper places significant emphasis on the importance of a real-time subtitle generator for Sinhala speech and explores the existing literature in the field. It outlines the objectives of the research and discusses the achieved outcomes. By fine-tuning hyperparameters to enhance the recognition accuracy of the system, impressive results of 88.28% training accuracy and 11.72% Word Error Rate (WER) are attained. Thesignificance of this research is underscored by its methodological advancements, robust performance metrics, and the potential impact on facilitating seamless interactions and applications in the Sinhala speech domain. Keywords: Speech recognition, Real-time, Subtitle, CMUSphinx, Open source, Hidden Markov Model, Mel-frequency cepstral coefficients, ’wav’, Accuracy, Word Error Rate

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“…Automatic speech transcription that is highly readable for humans is required for applications like automatic subtitle generation [2,3] and meeting minutes generation [4,5], while machine translation [6,7], dialogue systems [8,9], voice search [10,11], voice question answering [12,13], and many other applications require highly readable transcriptions to generate the best machine response. If the system is unable to deal with deficiencies in speech transcription, it will have a substantial negative impact on the application users' experience.…”

Section: Introductionmentioning

confidence: 99%

Automatic Speech Recognition Post-Processing for Readability: Task, Dataset and a Two-Stage Pre-Trained Approach

Liao

2022

IEEE Access

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Nowadays Automatic Speech Recognition (ASR) systems can accurately recognize which words are said. However, due to the disfluency, grammatical error, and other phenomena in spontaneous speech, the verbatim transcription of ASR impairs its readability, which is crucial for human comprehension and downstream tasks processing that need to understand the meaning and purpose of what is spoken. In this work, we formulate the ASR post-processing for readability (APR) as a sequence-to-sequence text generation problem that aims to transform the incorrect and noisy ASR output into readable text for humans and downstream tasks. We leverage the Metadata Extraction (MDE) corpus to construct a task-specific dataset for our study. To solve the problem of too little training data, we propose a novel data augmentation method that synthesizes large-scale training data from the grammatical error correction dataset. We propose a model based on the pre-trained language model to perform the APR task and train the model with a two-stage training strategy to better exploit the augmented data. On the constructed test set, our approach outperforms the best baseline system by a large margin of 17.53 on BLEU and 13.26 on readability-aware WER (RA-WER). The human evaluation also shows that our model can generate more human-readable transcripts than the baseline method.INDEX TERMS automatic post-editing, ASR post-processing for readability, data augmentation, pretrained language model, natural language processing

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Automatic Subtitle Generation for Videos

Cited by 12 publications

References 2 publications

Automated Caption Generation for Video Call with Language Translation

Automated Caption Generation for Video Call with Language Translation

Real-Time Subtitle Generator for Sinhala Speech

Automatic Speech Recognition Post-Processing for Readability: Task, Dataset and a Two-Stage Pre-Trained Approach

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