< Back
Posted: 30 Apr 2022 01:00

“Automatic Speech Recognition” April 2022 — summary from Astrophysics Data System and DOAJ

Brevi Assistant
Brevi Assistant

Business performance assistant

“Automatic Speech Recognition” April 2022 — summary from Astrophysics Data System and DOAJ main image

The content below is machine-generated by Brevi Technologies’ NLG model, and the source content was collected from open-source databases/integrate APIs.


Astrophysics Data System - summary generated by Brevi Assistant


In this paper, we conduct a comparative study on speaker-attributed automatic speech recognition in the multi-party meeting scenario, a subject with enhancing attention in meeting abundant transcription. To additionally mitigate the alignment problems, we recommend the 3rd strategy, TS-ASR, which educates a target-speaker splitting up module and an ASR module jointly.

Unpaired data has shown to be advantageous for low-resource automatic speech recognition ~, which can be associated with the style of hybrid models with multi-task training or language model dependent pre-training. Speculative results show that contrasted to speech-only training, the recommended basic CJT achieves wonderful performance improvements on clean/other test sets, and the CJT+ re-training returns additionally efficiency improvements.

The two most preferred loss functions for streaming end-to-end automatic speech recognition are the RNN-Transducer and the connectionist temporal category purposes. End-to-end models have achieved substantial renovation in automatic speech recognition. As automatic speech recognition systems are now being commonly deployed in the wild, the increasing threat of adversarial attacks increases major questions concerning the protection and reliability of using such systems. In this work, we investigate the impact of carrying out such multi-task learning on the adversarial toughness of ASR models in the speech domain.


Source texts:



DOAJ - summary generated by Brevi Assistant


We describe an FFT-based companding algorithm for preprocessing speech before recognition. In the Aurora-2 database taped with unnaturally added noise in a number of environments, the formula improves the relative word mistake rate in nearly all circumstances.

Automatic speech recognition, especially huge vocabulary constant speech recognition, is an important issue in the area of machine learning. Automatic speech recognition is a pattern recognition job in the area of computer technology, which is a discipline of Symmetry. The potential use Automatic Speech Recognition to assist responsive interaction is checked out. The chances and obstacles that this modern technology provides students and personnel to supply captioning of speech online or in classrooms for hard or deaf of hearing students and assist blind, visually impaired or dyslexic learners to review and look at learning material extra readily by increasing synthetic speech with all-natural documented real speech is also reviewed and assessed.

Automatic speech recognition is an essential technique of human- computer communications; gain control is a frequently used operation in ASR. First, by modeling the gain control method, the quantitative relationship between the gain control method and the ASR efficiency was established utilizing the noise figure index. This study is meant to help English as a Foreign Language students in Indonesia to decrease their stress and anxiety level while speaking before other individuals. This research disclosed that the product developed using ASR can make students exercise speaking separately without feeling pressurized and nervous.


This can serve as an example of how to use Brevi Assistant and integrated APIs to analyze text content.


Source texts:


logo

The Brevi assistant is a novel way to summarize, assemble, and consolidate multiple text documents/contents.

Partners:

© All rights reserved 2022 made by Brevi Technologies