A ROBUST ENSEMBLE MODEL FOR SPOKEN LANGUAGE RECOGNITION

A ROBUST ENSEMBLE MODEL FOR SPOKEN LANGUAGE RECOGNITION

Blog Article

The identity of a language being spoken has been tackled over the years via statistical models on audio samples.A drawback of these approaches is the unavailability of phonetically transcribed u11-200ps data for all languages.This work proposes an approach based on image classification that utilized image representations of audio samples.Our model used Neural Networks and deep learning algorithms to analyse and classify three languages.The input to our network is a Spectrogram that was processed through the networks to extract local visual and temporal features for language prediction.

From the model, we achieved 95.56 % accuracy borstlist självhäftande on the test samples from the 3 languages.