A unified deep neural network for speaker and language recognition
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Summary
The unified DNN approach is shown to yield substantial performance improvements on the the 2013 Domain Adaptation Challenge speaker recognition task and on the NIST 2011 Language Recognition Evaluation task.
- Type
- preprint
- Published
- 2015-04-03
- Cited by
- 163
- References
- 29
- Access
- Open access
- OpenAlex
- https://openalex.org/W1909308924
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2650546
Keywords
Computer science, Speech recognition, NIST, Speaker recognition, Word error rate
References
- Bottleneck features for speaker recognition
- The Kaldi Speech Recognition Toolkit
- Improving speaker recognition performance in the domain adaptation challenge using deep neural networks
- A novel scheme for speaker recognition using a phonetically-aware deep neural network
- Automatic language identification using deep neural networks
- Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets
- Inter dataset variability compensation for speaker recognition
- Convolutive Bottleneck Network features for LVCSR
- Domain adaptation via within-class covariance correction in I-vector based speaker recognition systems
- Extracting deep neural network bottleneck features using low-rank matrix factorization
- New types of deep neural network learning for speech recognition and related applications: an overview
- Combination of Cepstral and Phonetically Discriminative Features for Speaker Verification
- Front-End Factor Analysis for Speaker Verification
- i-vector representation based on bottleneck features for language identification
- Tandem connectionist feature extraction for conventional HMM systems
- SWITCHBOARD: telephone speech corpus for research and development
- i-Vector Modeling with Deep Belief Networks for Multi-Session Speaker Recognition
- Unsupervised Clustering Approaches for Domain Adaptation in Speaker Recognition Systems
- Deep Neural Networks for extracting Baum-Welch statistics for Speaker Recognition
- Application of Convolutional Neural Networks to Language Identification in Noisy Conditions
Cited by
- Automatic Dialect Detection in Arabic Broadcast Speech
- Deep Learning Backend for Single and Multisession i-Vector Speaker Recognition
- The IBM Speaker Recognition System: Recent Advances and Error Analysis
- On the use of deep feedforward neural networks for automatic language identification
- A phonetically aware system for speech activity detection
- Advanced b-vector system based deep neural network as classifier for speaker verification
- Cross-acoustic transfer learning for sound event classification
- Analysis of DNN approaches to speaker identification
- Exploring the role of phonetic bottleneck features for speaker and language recognition
- On the Use of Acoustic Unit Discovery for Language Recognition
- Optimisation de la consommation énergétique d'une ligne de métro automatique prenant en compte les aléas de trafic à l'aide d'outils d'intelligence artificielle
- Domain compensation based on phonetically discriminative features for speaker verification
- Spoofing Detection on the ASVspoof2015 Challenge Corpus Employing Deep Neural Networks
- LID-senone Extraction via Deep Neural Networks for End-to-End Language Identification
- Analysis and Optimization of Bottleneck Features for Speaker Recognition
- I-vector transformation and scaling for PLDA based speaker recognition
- Deep Language: a comprehensive deep learning approach to end-to-end language recognition
- Deep Neural Networks and Hidden Markov Models in i-vector-based Text-Dependent Speaker Verification
- Identification of British English regional accents using fusion of i-vector and multi-accent phonotactic systems
- Noise and Metadata Sensitive Bottleneck Features for Improving Speaker Recognition with Non-Native Speech Input
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