Keywords:
- acoustic modeling
- Adaboost
- Alzheimer's disease
- Anti-spoofing
- articulatory features
- Artificial Neural Networks
- atypical speech
- Automatic accent assessment
- Automatic accent evaluation
- automatic gender recognition
- Automatic speaker verification (ASV)
- Automatic Speech Recognition
- automatic subword unit derivation
- bag of audio words
- bandwidth
- Binary features
- binary masking
- bioacoustics
- BoAW
- boosting
- breathing pattern estimation
- breathing patterns
- call type classification
- call-type and caller classification
- Children speech recognition
- Classification
- CNN visualization
- ComParE features
- computational efficiency
- Conditional Random Fields
- confidence measures
- continuous speech recognition boosted binary features resource management
- Convolution Neural Network
- Convolutional neural network
- Convolutional Neural Networks
- COVID-19 identification
- cross-database
- cross-transfer knowledge
- Customer satisfaction
- deep learning
- deep neural networks
- depression detection
- Direction of arrival estimation
- dynamic programming
- Dysarthria
- Dysarthric speech
- embedding
- Emotion Recognition
- end-to-end acoustic modeling
- End-to-end learning
- end-to-end modelling
- end-to-end training
- expected performance and spoofability curve
- Expressive Vocalizations
- feature representations
- feature selection
- Few-shot learning
- fine-tuning
- fixed-size word patterns
- Formant identification
- Formants
- Foundation Model
- Fundamental frequency
- Fusion
- Gaussian mixture
- glottal source signals.
- grapheme
- Grapheme subword units
- grapheme subwords
- grapheme-to- phoneme conversion
- grapheme-to-phoneme conversion
- grapheme-to-phoneme converter
- Graphemes
- Hidden Markov Model
- hidden Markov models
- human skeleton estimation
- human speech
- integration of ASV and anti-spoofing
- Inter-pretable Models
- isolated word recognition
- Kalman filters
- KL-divergence
- KL-HMM
- Kullback-Leibler divergence
- Kullback-Leibler divergence based hidden Markov model
- Kullback-Leibler divergence based HMM
- Kullback–Leibler divergence based hidden Markov model
- language disorder
- Language Production
- Large Language Models
- letter-to-sound rules
- lexical model
- Lexical modeling
- Lexicon
- local posterior probability
- localization
- long-term statistics
- LoRA
- low level descriptors
- Mental Lexicon
- microphone array
- microphone arrays
- mobile biometrics
- modalities fusion
- modified ZFF
- multi- layer perceptron
- Multi-modal Approach
- multi-stream combination
- Multi-task learning
- multilayer perceptron
- multilayer perceptron network
- multilingual acoustic modeling
- multiple linear regression
- Multiple speaker localization
- multiple speakers
- multiple-stream combination
- multitask learning
- neural network
- neurocomputational models
- Noise Robustness
- non-native speech
- non-native speech recognition
- Objective Evaluation
- Objective intelligibility
- Objective intelligibility Assessment
- objective measures
- overlapping speech recognition
- Paralinguistic speech processing
- Parkinson's disease
- Parkinson's disease detection
- Parkinson’s disease
- parts-based approach
- Pathological speech
- Pathological Speech Processing
- Peft
- Perceived fluency
- phoneme
- phoneme modeling
- Phoneme recognition
- phoneme subword units
- phoneme subwords
- phonemes
- Phonetic information
- phonetic representation
- Phonocardiogram
- Posterior features
- posterior probabilities
- pre-trained embedding
- pre-training domain
- predictive coding
- presentation attack
- Presentation Attack Detection
- probabilistic lexical modeling
- pronunciation generation
- pronunciation lexicon
- Raw Speech
- raw waveform modelling
- raw waveforms
- raw-waveform cnn
- Reading Assessment
- recognition
- recurrent neural network
- Respiratory parameters
- S1-S2 detection
- Scottish Gaelic
- segment-level training.
- Self-Organizing Maps
- Self-supervised embedding
- self-supervised learning
- sign language assessment
- Sign language processing
- signal processing
- sleepiness
- speaker verification
- speaker-specific features
- spectral statistics
- Speech Analysis
- speech and audio
- speech assessment
- Speech breathing
- Speech Emotion Recognition
- Speech enhancement
- Speech for health
- Speech intelligibility
- speech pathology detection
- speech recognition
- speech recognition.
- speech separation
- speech synthesis
- Speech technology
- Spoken Language Understanding
- Spoofing
- spoofing detection
- Steered response power
- String matching
- SVM
- syllable-level-features
- syllables
- synthetic reference templates.
- Synthetic speech
- TANDEM features
- template-based approach
- template-based system
- Text classification
- text-to-speech synthesis
- tracking
- under-resource speech recognition
- under-resourced languages
- universal phoneme set
- unsupervised adaptation
- utterance verification
- voice activity detection
- Voice Conversion
- zero frequency filter
- Zero frequency filtering
- zero-frequency filtering
- zero-resourced speech recognition
Publications of Mathew Magimai.-Doss sorted by first author
M
| Using pitch frequency information in speech recognition, , and , in: Proceedings of Eurospeech, 2003 |
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| Using pitch frequency information in speech recognition, , and , Idiap-RR-23-2003 |
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| Modelling auxiliary information (pitch frequency) in hybrid HMM/ANN based ASR systems, , and , Idiap-RR-62-2002 |
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| Phoneme-Grapheme Based Speech Recognition System, , , and , in: Proceedings of IEEE ASRU, 2003 |
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| Phoneme-Grapheme Based Speech Recognition System, , , and , Idiap-RR-37-2003 |
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| Modelling Auxiliary Features in Tandem Systems, , , and , in: Proceedings of ICSLP, 2004 |
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| Modelling Auxiliary Features in Tandem Systems, , , and , Idiap-RR-21-2004 |
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| Juicer: A Weighted Finite-State Transducer speech decoder, , , , , and , in: 3rd Joint Workshop on Multimodal Interaction and Related Machine LEarning Algorithms MLMI'06, 2006 |
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| Juicer: A Weighted Finite-State Transducer speech decoder, , , , , and , Idiap-RR-21-2006 |
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| On Breathing Pattern Information in Synthetic Speech, and , in: Proceedings of Interspeech, 2022 |
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| Estimating Breathing Pattern from Raw Speech Waveform and Short-term Speech Spectrum using Neural Networks, , , , and , Idiap-RR-12-2024 |
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| On The Relationship Between Speech-based Breathing Signal Prediction Evaluation Measures And Breathing Parameters Estimation, , , , and , in: Proc. of ICASSP, 2021 |
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| Modeling Of Pre-trained Neural Network Embeddings Learned From Raw Waveform For Covid-19 Infection Detection, , , and , in: Proceedings of ICASSP, 2022 |
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| Understanding and Visualizing Raw Waveform-based CNNs, , , and , in: Proceedings of Interspeech, 2019 |
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| Gradient-based spectral visualization of CNNs using raw waveforms, , , and , Idiap-RR-11-2018 |
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| Long Term Spectral Statistics for Voice Presentation Attack Detection, , , and , Idiap-RR-11-2017 |
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| Long-Term Spectral Statistics for Voice Presentation Attack Detection, , , and , in: IEEE/ACM Transactions on Audio, Speech and Language Processing, 25(11):2098-2111, 2017 |
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| Towards directly modeling raw speech signal for speaker verification using CNNs, , and , in: IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, CANADA, pages 4884-4888, 2018 |
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| On Learning Vocal Tract System Related Speaker Discriminative Information from Raw Signal Using CNNs, , and , in: Proceedings of Interspeech, Hyderabad, INDIA, pages 1116-1120, 2018 |
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| End-to-End Convolutional Neural Network-based Voice Presentation Attack Detection, , and , in: International Joint Conference on Biometrics, Denver, Colorado, USA, 2017 |
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| Towards directly modeling raw speech signal for speaker verification using CNNs, , and , Idiap-RR-30-2017 |
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| Presentation Attack Detection Using Long-Term Spectral Statistics for Trustworthy Speaker Verification, , and , in: International Conference of the Biometrics Special Interest Group (BIOSIG), 2016 |
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| Towards interfacing large language models with ASR systems using confidence measures and prompting, , , , and , in: Proceedings of Interspeech, pages 2980-2984, 2024 |
[DOI] |
| Phoneme based Respiratory Analysis of Read Speech, , , and , in: Proceedings of European Signal Processing Conference (EUSIPCO), 2021 |
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| Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings, , , , and , in: Neural Networks, 141:211--224, 2021 |
[DOI] |
O
| A TDOA Gaussian Mixture Model for Improving Acoustic Source Tracking, , , and , Idiap-RR-10-2012 |
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| A TDOA Gaussian Mixture Model for Improving Acoustic Source Tracking, , , and , in: 20th European Signal Processing Conference, 2012 |
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| A Probabilistic Framework for Multiple Speaker Localization, , , and , in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013 |
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| Joint Detection and Localization of Multiple Speakers using a Probabilistic Interpretation of the Steered Response Power, , , and , in: Statistical and Perceptual Audition Workshop, 2012 |
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| A Probabilistic Framework for Multiple Speaker Localization, , , and , Idiap-RR-37-2012 |
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| Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks, , and , Idiap-RR-13-2013 |
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| Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks, , and , in: Proceedings of Interspeech, 2013 |
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| End-to-end Phoneme Sequence Recognition using Convolutional Neural Networks, , and , Idiap-RR-40-2013 |
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| End-to-End Acoustic Modeling using Convolutional Neural Networks for HMM-based Automatic Speech Recognition, , and , in: Speech Communication, 108:15--32, 2019 |
[DOI] |
| End-to-End Acoustic Modeling using Convolutional Neural Networks for Automatic Speech Recognition, , and , Idiap-RR-18-2016 |
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| Convolutional Neural Networks-based Continuous Speech Recognition using Raw Speech Signal, , and , in: International Conference on Acoustics, Speech and Signal Procecssing, IEEE, South Brisbane, QLD, pages 4295 - 4299, IEEE, 2015 |
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| Analysis of CNN-based Speech Recognition System using Raw Speech as Input, , and , Idiap-RR-23-2015 |
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| Learning linearly separable features for speech recognition using convolutional neural networks, , and , Idiap-RR-24-2015 |
[URL] |
| Analysis of CNN-based Speech Recognition System using Raw Speech as Input, , and , in: Proceedings of Interspeech, ISCA, Dresden, pages 11-15, ISCA, 2015 |
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| Raw Speech Signal-based Continuous Speech Recognition using Convolutional Neural Networks, , and , Idiap-RR-15-2014 |
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| Convolutional Neural Networks-based Continuous Speech Recognition using Raw Speech Signal, , and , Idiap-RR-18-2014 |
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