Linear Spectral Transformation for Robust Speech Recognition Using Maximum Mutual Information

2007 ◽  
Vol 14 (7) ◽  
pp. 496-499 ◽  
Author(s):  
Donghyun Kim ◽  
Dongsuk Yook
Keyword(s):  
Speech Recognition ◽  
Mutual Information ◽  
Robust Speech Recognition ◽  
Spectral Transformation ◽  
Maximum Mutual Information

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.

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