Implicit processing of LP residual for language identification. (January 2017)

• Record Type: Journal Article
• Authors: Nandi, Dipanjan; Pati, Debadatta; Rao, K. Sreenivasa
• Is Part Of: Computer speech & language. Volume 41(2016)
• Page Start: 68
• Page End: 87
• Publication Date: 2017-01
• Subjects: Hilbert envelope -- Residual phase -- LP residual -- Language identification (LID) -- Subsegmental -- Segmental -- Suprasegmental -- Excitation source information -- IITKGP-MLILSC -- OGI-MLTS -- Implicit processing of LP residual; 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.2016.06.002
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Excitation source information is explored for language identification. Implicit relations present in the LP residual samples are examined for LID task. The magnitude component of LP residual is explored for discriminating languages. The phase information present among LP residual samples is explored for LID task. Combined LID systems are developed using source features to enhance LID accuracy. Abstract: Present work explores the excitation source information for the language identification (LID) task. In this work, excitation source information is captured by implicit processing of linear prediction (LP) residual signal for discriminating the languages. Raw samples of LP residual signal, its magnitude, and phase components are processed independently at sub-segmental, segmental and suprasegmental levels for extracting the language-specific excitation source information. The LID studies are carried out using 27 Indian languages from Indian Institute of Technology Kharagpur-Multi Lingual Indian Language Speech Corpus (IITKGP-MLILSC) and 11 international languages from OGI-MLTS corpus. The Gaussian mixture models (GMMs) are used in this work to model the language-specific excitation source information for LID task. From the experimental results, it can be observed that, features extracted from segmental level yields better identification accuracy (50.92%), compared to sub-segmental (47.77%) and suprasegmental levels (43.88%). Further, the evidence from all three levels is combined to obtain the complete excitation source information. Finally, we have investigated the existence of non-overlapping language-specific information present in excitation source and vocal tract features.