Concept
Morphophonology
Related publications (8)
Phonetic aware techniques for Speaker Verification
The goal of this thesis is to improve current state-of-the-art techniques in speaker verification
(SV), typically based on âidentity-vectorsâ (i-vectors) and deep neural network (DNN), by exploiting diverse (phonetic) information extracted using variou ...
EPFL2018Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models
Ramya Rasipuram, Marzieh Razavi
Developing a phonetic lexicon for a language requires linguistic knowledge as well as human effort, which may not be available, particularly for under-resourced languages. An alternative to development of a phonetic lexicon is to automatically derive subwo ...
Idiap2017Acoustic data-driven grapheme-to-phoneme conversion in the probabilistic lexical modeling framework
Ramya Rasipuram, Marzieh Razavi
One of the primary steps in building automatic speech recognition (ASR) and text-to-speech systems is the development of a phonemic lexicon that provides a mapping between each word and its pronunciation as a sequence of phonemes. Phoneme lexicons can be d ...
2016Hierarchical approach for spotting keywords
The paper presents a new approach to spotting a particular sound (keyword) in an acoustic stream. The approach is based on hierarchical processing where equally-sampled posterior probabilities of phoneme classes are estimated first, followed by matched fil ...
IDIAP2005Using auxiliary sources of knowledge for automatic speech recognition
Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variatio ...
EPFL2005Using Auxiliary Sources of Knowledge for Automatic Speech Recognition
Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variatio ...
École Polytechnique Fédérale de Lausanne, Computer Science Department2005Using Auxiliary Sources of Knowledge for Automatic Speech Recognition
Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variatio ...
IDIAP2005Phoneme vs Grapheme Based Automatic Speech Recognition
Hervé Bourlard, Hynek Hermansky, John David Scott Dines
In recent literature, different approaches have been proposed to use graphemes as subword units with implicit source of phoneme information for automatic speech recognition. The major advantage of using graphemes as subword units is that the definition of ...
IDIAP2004