scholarly journals Model and feature based compensation for whispered speech recognition

2014 ◽  
Author(s):  
Shabnam Ghaffarzadegan ◽  
Hynek Bořil ◽  
John H. L. Hansen
Keyword(s):  
Speech Recognition ◽  
Whispered Speech ◽  
Feature Based

Optimization of Formant Feature Based Speech Recognition

Informatica ◽  
2010 ◽  
Vol 21 (3) ◽  
pp. 361-374 ◽  
Author(s):  
Antanas Lipeika
Keyword(s):  
Speech Recognition ◽  
Feature Based

scholarly journals Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System

2022 ◽  
Vol 14 (2) ◽  
pp. 614
Author(s):  
Taniya Hasija ◽  
Virender Kadyan ◽  
Kalpna Guleria ◽  
Abdullah Alharbi ◽  
Hashem Alyami ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Mutual Information ◽  
Data Augmentation ◽  
Recognition System ◽  
Speech Recognition System ◽  
Prosodic Features ◽  
Prosodic Feature ◽  
Feature Based ◽  
Maximum Mutual Information ◽  
Children's Speech

Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.

Whispered speech recognition using deep denoising autoencoder

2017 ◽  
Vol 59 ◽  
pp. 15-22 ◽  
Author(s):  
Đorđe T. Grozdić ◽  
Slobodan T. Jovičić ◽  
Miško Subotić
Keyword(s):  
Speech Recognition ◽  
Denoising Autoencoder ◽  
Whispered Speech

scholarly journals Normal and Whispered Speech Recognition Systems for Myanmar Digits

2018 ◽  
Vol 7 (11) ◽  
pp. 465-469
Author(s):  
Nyein Nyein Oo ◽  
Masaru Yamashita ◽  
Shoichi Matsunaga
Keyword(s):  
Speech Recognition ◽  
Whispered Speech ◽  
Recognition Systems
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