  In this work, we propose a new approach for language identification using
multi-head self-attention combined with raw waveform based 1D convolutional
neural networks for Indian languages. Our approach uses an encoder, multi-head
selfattention, and a statistics pooling layer. The encoder learns features
directly from raw waveforms using 1D convolution kernels and an LSTM layer. The
LSTM layer captures temporal information between the features extracted by the
1D convolutional layer. The multi-head self-attention layer takes outputs of
the LSTM layer and applies self-attention mechanisms on these features with M
different heads. This process helps the model give more weightage to the more
useful features and less weightage to the less relevant features. Finally, the
frame-level features are combined using a statistics pooling layer to extract
the utterance-level feature vector label prediction. We conduct all our
experiments on the 373 hrs of audio data for eight different Indian languages.
Our experiments show that our approach outperforms the baseline model by an
absolute 3.69% improvement in F1-score and achieves the best F1-score of
95.90%. Our approach also shows that using raw waveform models gets a 1.7%
improvement in performance compared to the models built using handcrafted
features.
