ISCA Archive SLTU 2014
ISCA Archive SLTU 2014

Modeling code-Switching speech on under-resourced languages for language identification

Koena Ronny Mabokela, Madimetja Jonas Manamela, Mabu Manaileng

This paper presents an integration of phonotactic information to perform language identification (LID) in a mixed-language speech. A single-pass front-end recognition system is employed to convert the spoken utterances into a statistical occurrence of phone sequences. To process such phone sequences, a hidden Markov model (HMM) is utilized to build robust acoustic models that can handle multiple languages within an utterance. A supervised Support Vector Machine (SVM) learns the language transition of the phonotactic information given the recognized phone sequences. The back-end SVM-based decision classifies language identity given the likelihood scores phone occurrences. The experiments are conducted on commonly mixed-language Northern Sotho and English speech utterances. We evaluate the system measuring the performance of the phone recognition and LID portions separately. We obtained a phone error rate of 15.7% when a data-driven phoneme mapping approach is modeled with 16 Gaussian mixtures per state. However, the proposed integrated LID system has achieved a considerable performance with an acceptable LID accuracy of 85.0% and average of 81% on code-switched speech and monolingual speech segments respectively.

Index Terms: Code-switching speech, under-resourced languages, phonotactic information, acoustic models, language model


Cite as: Mabokela, K.R., Manamela, M.J., Manaileng, M. (2014) Modeling code-Switching speech on under-resourced languages for language identification. Proc. 4th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2014), 225-230

@inproceedings{mabokela14_sltu,
  author={Koena Ronny Mabokela and Madimetja Jonas Manamela and Mabu Manaileng},
  title={{Modeling code-Switching speech on under-resourced languages for language identification}},
  year=2014,
  booktitle={Proc. 4th Workshop on Spoken Language Technologies for Under-Resourced Languages  (SLTU 2014)},
  pages={225--230}
}