Language identification from visual-only speech signals

Rebecca E. Ronquest; Susannah V. Levi; David B. Pisoni

doi:10.3758/app.72.6.1601

Language identification from visual-only speech signals

Attention Perception & Psychophysics ◽

10.3758/app.72.6.1601 ◽

2010 ◽

Vol 72 (6) ◽

pp. 1601-1613 ◽

Cited By ~ 18

Author(s):

Rebecca E. Ronquest ◽

Susannah V. Levi ◽

David B. Pisoni

Keyword(s):

Language Identification ◽

Speech Signals

Download Full-text

FuzzyGCP: A deep learning architecture for automatic spoken language identification from speech signals

Expert Systems with Applications ◽

10.1016/j.eswa.2020.114416 ◽

2021 ◽

Vol 168 ◽

pp. 114416

Author(s):

Avishek Garain ◽

Pawan Kumar Singh ◽

Ram Sarkar

Keyword(s):

Deep Learning ◽

Spoken Language ◽

Language Identification ◽

Speech Signals

Download Full-text

Automatic Language Identification Using Speech Rhythm Features for Multi-Lingual Speech Recognition

Applied Sciences ◽

10.3390/app10072225 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2225

Author(s):

Hwamin Kim ◽

Jeong-Sik Park

Keyword(s):

Speech Recognition ◽

Gaussian Mixture ◽

Language Identification ◽

Speech Signals ◽

General Tendency ◽

Specific Information ◽

Acoustic Features ◽

Computation Complexity ◽

Speech Rhythm ◽

Speech Corpora

The conventional speech recognition systems can handle the input speech of a specific single language. To realize multi-lingual speech recognition, a language should be firstly identified from input speech. This study proposes an efficient Language IDentification (LID) approach for the multi-lingual system. The standard LID tasks depend on common acoustic features used in speech recognition. However, the features may convey insufficient language-specific information, as they aim to discriminate the general tendency of phonemic information. This study investigates another type of feature characterizing language-specific properties, considering computation complexity. We focus on speech rhythm features providing the prosodic characteristics of speech signals. The rhythm features represent the tendency of consonants and vowels of languages, and therefore, classifying them from speech signals is necessary. For the rapid classification, we employ Gaussian Mixture Model (GMM)-based learning in which two GMMs corresponding to consonants and vowels are firstly trained and used for classifying them. By using the classification results, we estimate the tendency of two phonemic groups such as the duration of consonantal and vocalic intervals and calculate rhythm metrics called R-vector. In experiments on several speech corpora, the automatically extracted R-vector provided similar language tendencies to the conventional studies on linguistics. In addition, the proposed R-vector-based LID approach demonstrated superior or comparable LID performance to the conventional approaches in spite of low computation complexity.

Download Full-text