Multi-Language Identification Using Convolutional Recurrent Neural Network

2016/11/12 by Vrishabh Ajay Lakhani, Lakhani, Vrishabh Ajay, Rohan Mahadev +1
Computer Science · #Authorship Attribution and Profiling #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis

paper · pdf · doi:10.48550/arxiv.1611.04010

openalex publication_date 2016/11/12 · openalex created_date 2016/11/30 · openalex updated_date 2026/07/28

Abstract

Language Identification, being an important aspect of Automatic Speaker Recognition has had many changes and new approaches to ameliorate performance over the last decade. We compare the performance of using audio spectrum in the log scale and using Polyphonic sound sequences from raw audio samples to train the neural network and to classify speech as either English or Spanish. To achieve this, we use the novel approach of using a Convolutional Recurrent Neural Network using Long Short Term Memory (LSTM) or a Gated Recurrent Unit (GRU) for forward propagation of the neural network. Our hypothesis is that the performance of using polyphonic sound sequence as features and both LSTM and GRU as the gating mechanisms for the neural network outperform the traditional MFCC features using a unidirectional Deep Neural Network.

Citations

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• ImageNet classification with deep convolutional neural networks

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