Multi-dialectical Languages Effect On Speech Recognition Too Much Choice Can Hurt
Article Sidebar
Main Article Content
Abstract
Research has shown that automatic speech recognition (ASR) performance typically decreases when evaluated on a dialectal variation of the same language that was not used for training its models. Similarly, models simultaneously trained on a group of dialects tend to underperform when compared to dialect-specific models. When trying to decide which dialect-specific model (recognizer) to use to decode an utterance (e.g., a voice search query), possible strategies include automatically detecting the spoken dialect or following the user's language preferences as set in his/her cell phone. In this paper, we observe that user's voice search queries are usually directed to a dialect-specific recognizer that does not match the user's current location, and present a study that shows that automatically selecting the recognizer based on the user's geographical location helps improve the user experience.
Article Details
References
M.Zissman et al. Automatic dialect identification of extemporaneous conversational.Latin American Spanish speech.ICASSP1996, pp.777-780.
W.Shen, N.Chen , and D.Reynolds.Dialect recognition using adapted phonetic models, INTERSPEECH 2008.PP.763-766.
F.Biadsy , J.Hirschberg and N.Habash, spoken Arabic Dialect Identification using phonotactic modeling. EACL 2009 WORKSHOP on computational study on hamburg and berlin german, Language and speech 45.2(2002), pp.115-138.
R.Hamdi et al . Speech timing and rhythmic structure in Arabic dialect: a comparaison of two approaches , interspeech 2004.
F.Biadsy , Automatic Dialect and accent recognition and its application to speech recognition, phd thesis, Columbia u,, 2011.
F.Biadsy , and j.Hirschberg, Using prosody and phonotatics in Arabic dialect identification, INTERSPEECH2009.
S.Ghazali, R.Hamdi, and M.Barkat, Speech rhythm variation in Arabic dialect, International conference on speech prosody,2002.
M.Belgacem ,G.Antoniadis, and L.Besacier, Automatic identification of Arabic dialects, LREC2010.
Lopez-Moreno, J.Gonzalez-Dominguez, O.plchot, D.Martinez, J.Gonzalez-Rodriguez, and P.Moreno ,Automatic language identification using long short-term memory recurrent neural networks, Interspeech 2014,pp.2155-2159.