- Correction to: Performance analysis of neural network, NMF and statistical approaches for speech enhancement
- Correction to: Revisiting distinctive phonetic features from applied computing perspective: unifying views and analyzing modern Arabic speech varieties
- Correction to: Low-complexity disordered speech quality estimation
- Correction to: Voice recognition package for ERTU’s cloud
- Text-Independent Speaker Verification for Real Fast-Varying Noisy Environments
- Correction to: Production of referring expressions in Arabic
- Extraction of Speaker Features from Different Stages of DSR Front-Ends for Distributed Speaker Verification
- Enhancing the Performance of Gaussian Mixture Model-Based Text Independent Speaker Identification
- Note from the editor: Special issue on speaker recognition
- Pattern analysis based acoustic signal processing: a survey of the state-of-art
- Speech emotion recognition research: an analysis of research focus
- Databases, features and classifiers for speech emotion recognition: a review
- A review on speech processing using machine learning paradigm
- Fundamentals, present and future perspectives of speech enhancement
- Difference expansion based reversible watermarking algorithms for copyright protection of images: state-of-the-art and challenges
- Automatic speaker verification systems and spoof detection techniques: review and analysis
- Choice of a classifier, based on properties of a dataset: case study-speech emotion recognition
- [springer.com] i-Vectors in speech processing applications: a survey
- Meta-heuristic approach in neural network for stress detection in Marathi speech
- A review of supervised learning algorithms for single channel speech enhancement
- Expressive speech synthesis: a review
- Front end analysis of speech recognition: a review
- An overview of digital speech watermarking
- Are chatbots really useful for human resource management?
- An efficient framework of utilizing the latent semantic analysis in text extraction
- A novel voice conversion approach using cascaded powerful cepstrum predictors with excitation and phase extracted from the target training space encoded as a KD-tree
- A noise robust speech features extraction approach in multidimensional cortical representation using multilinear principal component analysis
- Speaker-independent expressive voice synthesis using learning-based hybrid network model
- Development of speech corpora for speaker recognition research and evaluation in Indian languages
- Emotion recognition using semi-supervised feature selection with speaker normalization
- Convolutional neural network vectors for speaker recognition
- Supervector-based approaches in a discriminative framework for speaker verification in noisy environments
- Multistage classification scheme to enhance speech emotion recognition
- Neural network based feature transformation for emotion independent speaker identification
- Efficient text summarization method for blind people using text mining techniques
- Recent developments in spoken term detection: a survey
- Speaker recognition under stressed condition
- [springer.com] Improving Arabic information retrieval using word embedding similarities
- Pitch adaptive MFCC features for improving children’s mismatched ASR
- A decision tree using ID3 algorithm for English semantic analysis
- Vocal-based emotion recognition using random forests and decision tree
- Designing of Gabor filters for spectro-temporal feature extraction to improve the performance of ASR system
- Continuous Tamil Speech Recognition technique under non stationary noisy environments
- Continuous speech recognition and syntactic processing in Iranian Farsi language
- Enhancement in speaker recognition for optimized speech features using GMM, SVM and 1-D CNN
- Efficient anomaly detection from medical signals and images
- Combining evidences from Hilbert envelope and residual phase for detecting replay attacks
- A new adaptive solution based on joint acoustic noise and echo cancellation for hands-free systems
- Enhanced speech emotion detection using deep neural networks
- Convolutional support vector machines for speech recognition
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