CONF
Motlicek_ICASSP2013-2_2013/IDIAP
ACCENT ADAPTATION USING SUBSPACE GAUSSIAN MIXTURE MODELS
Motlicek, Petr
Garner, Philip N.
Kim, Namhoon
Cho, Jeongmi
EXTERNAL
https://publications.idiap.ch/attachments/papers/2013/Motlicek_ICASSP2013-2_2013.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/Motlicek_Idiap-RR-38-2013
Related documents
IEEE - The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Vancouver, BC, Canada
2013
7170-7174
1520-6149
10.1109/ICASSP.2013.6639054
doi
This paper investigates employment of Subspace Gaussian Mixture Models (SGMMs) for acoustic model adaptation towards different accents for English speech recognition. The SGMMs comprise globally-shared and state-specific parameters which can efficiently be employed for various kinds of acoustic parameter tying. Research results indicate that well-defined sharing of acoustic model parameters in SGMMs can significantly outperform adapted systems based on conventional HMM/GMMs. Furthermore, SGMMs rapidly achieve target acoustic models with small amounts of data. Experiments performed with US and UK English versions of the
Wall Street Journal (WSJ) corpora indicate that SGMMs lead to approximately 20% and 8% relative improvements with respect to speaker-independent and speaker-adapted acoustic models respectively over conventional HMM/GMMs. Finally, we demonstrate that SGMMs adapted only with 1.5 hours can reach performance of HMM/GMMs trained with 18 hours.
REPORT
Motlicek_Idiap-RR-38-2013/IDIAP
ACCENT ADAPTATION USING SUBSPACE GAUSSIAN MIXTURE MODELS
Motlicek, Petr
Garner, Philip N.
Kim, Namhoon
Cho, Jeongmi
Accented speech
Acoustic model adaptation
Automatic Speech Recognition
Under-resourced data
EXTERNAL
https://publications.idiap.ch/attachments/reports/2013/Motlicek_Idiap-RR-38-2013.pdf
PUBLIC
Idiap-RR-38-2013
2013
Idiap
Rue Marconi 19, Martigny, Switzerland
November 2013
This paper investigates employment of Subspace Gaussian Mixture Models (SGMMs) for acoustic model adaptation towards different accents for English speech recognition. The SGMMs comprise globally-shared and state-specific parameters which can efficiently be employed for various kinds of acoustic parameter tying. Research results indicate that well-defined sharing of acoustic model parameters in SGMMs can significantly outperform adapted systems based on conventional HMM/GMMs. Furthermore, SGMMs rapidly achieve target acoustic models with small amounts of data. Experiments performed with US and UK English versions of the
Wall Street Journal (WSJ) corpora indicate that SGMMs lead to approximately 20% and 8% relative improvements with respect to speaker-independent and speaker-adapted acoustic models respectively over conventional HMM/GMMs. Finally, we demonstrate that SGMMs adapted only with 1.5 hours can reach performance of HMM/GMMs trained with 18 hours.