Unsupervised crosslingual adaptation of tokenisers for spoken language recognition. (November 2017)

• Record Type: Journal Article
• Authors: Ng, Raymond W.M.; Nicolao, Mauro; Hain, Thomas
• Is Part Of: Computer speech & language. Volume 46(2017)
• Page Start: 327
• Page End: 342
• Publication Date: 2017-11
• Subjects: Language recognition -- Unsupervised adaptation -- Crosslingual adaptation -- Tokenisation -- Phonotactic SLR; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.05.002
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: 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.