Improving Automatic Speech Recognition and Speech Translation via Word Embedding Prediction

Chuang, Shun-Po; Liu, Alexander H.; Sung, Tzu-Wei; Lee, Hung-yi

doi:10.1109/taslp.2020.3037543

Cited by 11 publications

(3 citation statements)

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“…Information retrieval, question answering, machine translation, and other downstream applications use NER as a pre-processing step. In an end-to-end multitasking context, word embedding methods like Word2Vec and fastText are used to improve speech translation (Chuang et al 2021 ). Cross domains adversarial learning models comprised of CNN, BiLSTM, and Word2Vec embedding are utilized to categorize the information from EHR available in the Chinese language and achieve F1-score of 74.39%.…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

“… Catelli et al ( 2020 ) Italian language EHR classification English i2b2 2014 de-identification corpus, the Italian SIRM COVID-19 de-identification corpus Bi-LSTM BERT, MultiBPEmb, and Flair Multilingual Fast embeddings MultiBPEmb + Flair multi-fast achieves a micro F1-score of 94.48% 6. Chuang et al ( 2021 ) Automatic speech recognition and translation system LibriSpeech corpus, Augmented LibriSpeech, Fisher Spanish corpora LSTM, BiLSTM, CNN Word2Vec, fastText Word2Vec model efficiently maps speech signals to semantic space 7. Zhang et al ( 2021 ) Chinese word representation SogouCA data, Wikipedia dump, Fudan dataset LSTM Word2Vec, GloVe, BERT, CWE LSTM + CWE achieves an F1-score of 95.53% for the NER task 8.…”

Section: Appendix Amentioning

confidence: 99%

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Impact of word embedding models on text analytics in deep learning environment: a review

2023

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The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

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Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

Section: Appendix Amentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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“…At present, the embedded speech recognition system based on the microprocessor platform can train from the speech library, create a training model, and complete the recognition by combining the human voice and pattern, rather than a specific person for speech recognition. In this paper, DTW algorithm combined with polynomial kernel function is used to effectively process the time-varying features of embedded speech, and a new PDTW-SVM algorithm is obtained [2]. It has local interpolation capabilities and global generalization capabilities, as well as the time-varying characteristics of speech signals.…”

Section: Introductionmentioning

confidence: 99%

Application of embedded voice and digital forensics system in financial cost management

Lin

2023

Preprint

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This article first describes the embedded voice processing process, and uses the two-door restriction method for endpoint detection. Next, the wavelet+MFCC algorithm is proposed. The Fourier transform will replace it with ripple. The disadvantage of the ripple is that the size of the Fourier transform window cannot move with frequency. On this basis, the overall goal of the system is proposed, the overall structure is constructed, and the functions of the system are divided. Android mobile phone data collection technology has been extensively researched, including logical search, temporary access to permissions, JTAG search and ISP search. The design and implementation of the logical search engine based on root authority, the physical search engine based on Qualcomm processor, and the data analysis system have been completed, and the evidence collection of e-commerce applications has been completed. Based on the digital forensics system, this article studies and designs an Android device digital forensics system suitable for China's national conditions and laws from the three perspectives of data collection, analysis and storage. At the same time, brainstorming methods must be used between corporate financial management and cost management personnel. Finally, through research, scientifically construct the framework of the financial cost management system, distinguish the various requirements of the construction system, classify the importance, urgency, and feasibility of the requirements, establish the basic requirements and extension requirements, set the demand function to complete the schedule and Perform as planned. Through the research of embedded voice and digital forensics system, this paper applies it to the research of financial cost management, thereby promoting the development of enterprise financial management.

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A Comprehensive Review of Recent Automatic Speech Summarization and Keyword Identification Techniques

Kumar

Mahrishi

Meena

2021

Learning and Analytics in Intelligent Systems

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Improving Automatic Speech Recognition and Speech Translation via Word Embedding Prediction

Cited by 11 publications

References 55 publications

Impact of word embedding models on text analytics in deep learning environment: a review

Impact of word embedding models on text analytics in deep learning environment: a review

Application of embedded voice and digital forensics system in financial cost management

A Comprehensive Review of Recent Automatic Speech Summarization and Keyword Identification Techniques

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