8 research outputs found
Unsupervised crosslingual adaptation of tokenisers for spoken language recognition
Phone tokenisers are used in spoken language recognition (SLR) to obtain elementary
phonetic information. We present a study on the use of deep neural
network tokenisers. Unsupervised crosslingual adaptation was performed to
adapt the baseline tokeniser trained on English conversational telephone speech
data to different languages. Two training and adaptation approaches, namely
cross-entropy adaptation and state-level minimum Bayes risk adaptation, were
tested in a bottleneck i-vector and a phonotactic SLR system. The SLR systems
using the tokenisers adapted to different languages were combined using score
fusion, giving 7-18% reduction in minimum detection cost function (minDCF)
compared with the baseline configurations without adapted tokenisers. Analysis
of results showed that the ensemble tokenisers gave diverse representation of
phonemes, thus bringing complementary effects when SLR systems with different
tokenisers were combined. SLR performance was also shown to be related
to the quality of the adapted tokenisers
Unsupervised crosslingual adaptation of tokenisers for spoken language recognition
Phone tokenisers are used in spoken language recognition (SLR) to obtain elementary
phonetic information. We present a study on the use of deep neural
network tokenisers. Unsupervised crosslingual adaptation was performed to
adapt the baseline tokeniser trained on English conversational telephone speech
data to different languages. Two training and adaptation approaches, namely
cross-entropy adaptation and state-level minimum Bayes risk adaptation, were
tested in a bottleneck i-vector and a phonotactic SLR system. The SLR systems
using the tokenisers adapted to different languages were combined using score
fusion, giving 7-18% reduction in minimum detection cost function (minDCF)
compared with the baseline configurations without adapted tokenisers. Analysis
of results showed that the ensemble tokenisers gave diverse representation of
phonemes, thus bringing complementary effects when SLR systems with different
tokenisers were combined. SLR performance was also shown to be related
to the quality of the adapted tokenisers
Acoustic Modelling for Under-Resourced Languages
Automatic speech recognition systems have so far been developed only for very few languages out of the 4,000-7,000 existing ones.
In this thesis we examine methods to rapidly create acoustic models in new, possibly under-resourced languages, in a time and cost effective manner. For this we examine the use of multilingual models, the application of articulatory features across languages, and the automatic discovery of word-like units in unwritten languages
Automated phoneme mapping for cross-language speech recognition
This dissertation explores a unique automated approach to map one phoneme set to another, based on the acoustic distances between the individual phonemes. Although the focus of this investigation is on cross-language applications, this automated approach can be extended to same-language but different-database applications as well. The main goal of this investigation is to be able to use the data of a source language, to train the initial acoustic models of a target language for which very little speech data may be available. To do this, an automatic technique for mapping the phonemes of the two data sets must be found. Using this technique, it would be possible to accelerate the development of a speech recognition system for a new language. The current research in the cross-language speech recognition field has focused on manual methods to map phonemes. This investigation has considered an English-to-Afrikaans phoneme mapping, as well as an Afrikaans-to-English phoneme mapping. This has been previously applied to these language instances, but utilising manual phoneme mapping methods. To determine the best phoneme mapping, different acoustic distance measures are compared. The distance measures that are considered are the Kullback-Leibler measure, the Bhattacharyya distance metric, the Mahalanobis measure, the Euclidean measure, the L2 metric and the Jeffreys-Matusita distance. The distance measures are tested by comparing the cross-database recognition results obtained on phoneme models created from the TIMIT speech corpus and a locally-compiled South African SUN Speech database. By selecting the most appropriate distance measure, an automated procedure to map phonemes from the source language to the target language can be done. The best distance measure for the mapping gives recognition rates comparable to a manual mapping process undertaken by a phonetic expert. This study also investigates the effect of the number of Gaussian mixture components on the mapping and on the speech recognition system’s performance. The results indicate that the recogniser’s performance increases up to a limit as the number of mixtures increase. In addition, this study has explored the effect of excluding the Mel Frequency delta and acceleration cepstral coefficients. It is found that the inclusion of these temporal features help improve the mapping and the recognition system’s phoneme recognition rate. Experiments are also carried out to determine the impact of the number of HMM recogniser states. It is found that single-state HMMs deliver the optimum cross-language phoneme recognition results. After having done the mapping, speaker adaptation strategies are applied on the recognisers to improve their target-language performance. The models of a fully trained speech recogniser in a source language are adapted to target-language models using Maximum Likelihood Linear Regression (MLLR) followed by Maximum A Posteriori (MAP) techniques. Embedded Baum-Welch re-estimation is used to further adapt the models to the target language. These techniques result in a considerable improvement in the phoneme recognition rate. Although a combination of MLLR and MAP techniques have been used previously in speech adaptation studies, the combination of MLLR, MAP and EBWR in cross-language speech recognition is a unique contribution of this study. Finally, a data pooling technique is applied to build a new recogniser using the automatically mapped phonemes from the target language as well as the source language phonemes. This new recogniser demonstrates moderate bilingual phoneme recognition capabilities. The bilingual recogniser is then further adapted to the target language using MAP and embedded Baum-Welch re-estimation techniques. This combination of adaptation techniques together with the data pooling strategy is uniquely applied in the field of cross-language recognition. The results obtained using this technique outperform all other techniques tested in terms of phoneme recognition rates, although it requires a considerably more time consuming training process. It displays only slightly poorer phoneme recognition than the recognisers trained and tested on the same language database.Dissertation (MEng (Computer Engineering))--University of Pretoria, 2006.Electrical, Electronic and Computer Engineeringunrestricte