A Real-Time DSP-Based System for Voice Activity Detection and Background Noise Reduction

Singh, Charu; Venter, Maarten; M, Rajesh Kumar; Brown, David

doi:10.1016/b978-0-12-818130-0.00003-9

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several algorithms and applications are available for speech enhancement. We performed noise suppression using spectral subtraction and voice activity detection [37] over noisy audio samples. We have obtained spectral estimates for the background noise from the input signal.…”

Section: Noise Reductionmentioning

confidence: 99%

Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning

et al. 2021

View full text Add to dashboard Cite

Automatic speech recognition for Arabic has its unique challenges and there has been relatively slow progress in this domain. Specifically, Classic Arabic has received even less research attention. The correct pronunciation of the Arabic alphabet has significant implications on the meaning of words. In this work, we have designed learning models for the Arabic alphabet classification based on the correct pronunciation of an alphabet. The correct pronunciation classification of the Arabic alphabet is a challenging task for the research community. We divide the problem into two steps, firstly we train the model to recognize an alphabet, namely Arabic alphabet classification. Secondly, we train the model to determine its quality of pronunciation, namely Arabic alphabet pronunciation classification. Due to the less availability of audio data of this kind, we had to collect audio data from the experts, and novices for our model’s training. To train these models, we extract pronunciation features from audio data of the Arabic alphabet using mel-spectrogram. We have employed a deep convolution neural network (DCNN), AlexNet with transfer learning, and bidirectional long short-term memory (BLSTM), a type of recurrent neural network (RNN), for the classification of the audio data. For alphabet classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 95.95%, 98.41%, and 88.32%, respectively. For Arabic alphabet pronunciation classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 97.88%, 99.14%, and 77.71%, respectively.

show abstract

Section: Noise Reductionmentioning

confidence: 99%