Robust Recognition of Tone Specified Mizo Digits Using CNN-LSTM and Nonlinear Spectral Resolution

In this work, we attempt Mizo digit recognition under degraded conditions, using spectrograms as visual inputs to a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network. As a tone language, each digit in Mizo is associated with a specific sequence of tones. To emphasize the tonal information, low frequency resolution is increased by applying a nonlinear spectral resolution method. The use of nonlinear spectral resolution improves the recognition rate of the system, as evident from the word error rate decrease of about 4% when the training data contains speech data with similar noise profiles as in the testing data. When the training data is clean, improvement in recognition rate is about 2%, using the nonlinear spectral resolution method. The proposed method, compared with the Deep Neural Network-Hidden Markov Model (DNN-HMM) based baseline system, gives an improvement of around 40% and 15% for 0 dB and 5 dB SNRs, respectively when noise profiles of speech sounds included in training and testing conditions are similar.