Acoustic modeling for under-resourced languages based on vectorial HMM-states representation using Subspace Gaussian Mixture Models

Abstract : This paper explores a novel method for context-dependent models in automatic speech recognition (ASR), in the context of under-resourced languages. We present a simple way to realize a tying states approach, based on a new vectorial representation of the HMM states. This vectorial representation is considered as a vector of a low number of parameters obtained by the Subspace Gaussian Mixture Models paradigm (SGMM). The proposed method does not require phonetic knowledge or a large amount of data, which represent the major problems of acoustic modeling for under-resourced languages. This paper shows how this representation can be obtained and used for tying states. Our experiments, applied on Vietnamese, show that this approach achieves a stable gain compared to the classical approach which is based on decision trees. Furthermore, this method appears to be portable to other languages, as shown in the preliminary study conducted on Berber.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01313103
Contributor : Bibliothèque Universitaire Déposants Hal-Avignon <>
Submitted on : Monday, May 9, 2016 - 3:34:12 PM
Last modification on : Tuesday, July 2, 2019 - 5:38:02 PM

Identifiers

Collections

Citation

Mohamed Bouallegue, Emmanuel Ferreira, Driss Matrouf, Georges Linarès, Maria Goudi, et al.. Acoustic modeling for under-resourced languages based on vectorial HMM-states representation using Subspace Gaussian Mixture Models. IEEE Spoken Language Technology Workshop (SLT), Dec 2012, Miami, United States. ⟨10.1109/SLT.2012.6424245⟩. ⟨hal-01313103⟩

Share

Metrics

Record views

84