Cross-Lingual Speaker Identification for Indian Languages
Amaan Rizvi, Anupam Jamatia, Dwijen Rudrapal, Kunal Chakma, Björn Gambäck · 2023
The paper introduces a cross-lingual speaker identification system for Indian languages, utilising a Long Short-Term Memory dense neural network (LSTM-DNN).The system was trained on audio recordings in English and evaluated on data from Hindi, Kannada, Malayalam, Tamil, and Telugu, with a view to how factors such as phonetic similarity and native accent affect performance.The model was fed with MFCC (mel-frequency cepstral coefficient) features extracted from the audio file.For comparison, the corresponding melspectrogram images were also used as input to a ResNet-50 model, while the raw audio was used to train a Siamese network.The LSTM-DNN model outperformed the other two models as well as two more traditional baseline speaker identification models, showing that deep learning models are superior to probabilistic models for capturing low-level speech features and learning speaker characteristics.