This paper describes the implementation of HMM (Hidden Markov Model) based speaker independent isolated word Automatic Speech Recognition (ASR) system for Nepali Language, a commonly spoken language in Nepal. The system has been developed in python using numpy[1] and YAHMM[2] libraries. The system is trained in different Nepali words by collecting data from different speakers in room environment. The tests have also been carried out in similar setup. This paper details the experiment by discussing the concept, implementation details and overall interpretation of the system. The experimental results show that the overall accuracy of the presented system is about 75%.

HMM based isolated word Nepali speech recognition

Development of Automatic Speech Recognition system for Kazakh language is very challenging due to a lack of data.Existing data of kazakh speech with its corresponding transcriptions are heavily accessed and not enough to gain a worth mentioning results.For this reason, speech recognition of Kazakh language has not been explored well.There are only few works that investigate this area with traditional methods Hidden Markov Model, Gaussian Mixture Model, but they are suffering from poor outcome and lack of enough this http URL our work we suggest a new method that takes pre-trained model of Russian language and applies its knowledge as a starting point to our neural network structure, which means that we are transferring the weights of pre-trained model to our neural network.The main reason we chose Russian model is that pronunciation of kazakh and russian languages are quite similar because they share 78 percent letters and there are quite large corpus of russian speech dataset. We have collected a dataset of Kazakh speech with transcriptions in the base of Suleyman Demirel University with 50 native speakers each having around 400 sentences.Data have been chosen from famous Kazakh books. 
We have considered 4 different scenarios in our experiment. First, we trained our neural network without using a pre-trained Russian model with 2 LSTM layers and 2 BiLSTM .Second, we have trained the same 2 LSTM layered and 2 BiLSTM layered using a pre-trained model. As a result, we have improved our models training cost and Label Error Rate by using external Russian speech recognition model up to 24 percent and 32 percent respectively.Pre-trained Russian language model has trained on 100 hours of data with the same neural network architecture.

Development of Automatic Speech Recognition for Kazakh Language using Transfer Learning.

To explore the diversified human-machine interaction methods in the main control room (MCR) of nuclear power plants (NPP), this project established a complete set of intelligent speech control system based on Mandarin, and created a speech training system based on MFCC (Mel-Frequency Cepstral Coefficients) features and CTC (Connectionist Temporal Classification) related algorithm. This project has further enhanced the multilingual pronunciation sentences recognition rate, strengthened the logical connection between English letters, Chinese characters and numbers. Using the speech training set after training and noise reduction, when recognizing mixed sentences of Chinese characters, English letters, and numbers, this system can achieve a speech recognition accuracy rate higher than 93% and reduce the time spent on tasks by an average of 70%.

Artificial Intelligence and Machine Learning Application in NPP MCR Speech Monitoring System

This paper presents an end-to-end deep learning model for Automatic Speech Recognition (ASR) that transcribes Nepali speech to text. The model was trained and tested on the OpenSLR (audio, text) dataset. The majority of the audio dataset have silent gaps at both ends which are clipped during dataset preprocessing for a more uniform mapping of audio frames and their corresponding texts. Mel Frequency Cepstral Coe cients (MFCCs) are used as audio features to feed into the model. The model having Bidirectional LSTM paired with ResNet and one-dimensional CNN produces the best results for this dataset out of all the models (neural networks with variations of LSTM, GRU, CNN, and ResNet) that have been trained so far. This novel model uses Connectionist Temporal Classification (CTC) function for loss calculation during training and CTC beam search decoding for predicting characters as the most likely sequence of Nepali text. On the test dataset, the character error rate (CER) of 17.06 percent has been achieved. 

Automatic speech recognition for the Nepali language using CNN, bidirectional LSTM and ResNet

Speech Recognition is one of the prominent research topics in the field of Natural Language Processing (NLP). The Speech Recognition technique removes the barriers and makes the system ease for inter-communication between human beings and devices. The aim of this study is to analyze the Automatic Speech Recognition System (ASRS) proposed by different researchers using Machine learning and Deep Learning techniques. In this work, Indian and foreign languages speech recognition systems like Hindi, Marathi, Malayalam, Urdu, Sanskrit, Nepali, Kannada, Chinese, Japanese, Arabic, Italian, Turkish, French, and German are considered. An integrated framework is presented and elaborated with recent advancement. The various platform like Hidden Markov Model Toolkit (HMM Toolkit), CMU Sphinx, Kaldi toolkit are explained which is used for building the speech recognition model. Further, some applications are elaborated which depict the uses of ASRS.

An automatic speech recognition system in Indian and foreign languages: A state-of-the-art review analysis

This paper presents a Neural Network based Nepali Speech Recognition model. RNN (Recurrent Neural Networks) is used for processing sequential audio data. CTC (Connectionist Temporal Classification) [1] technique is applied allowing RNN to train over audio data. CTC is a probabilistic approach of maximizing the occurrence probability of the desired labels from RNN output. After processing through RNN and CTC layers, Nepali text is obtained as output. This paper also defines a character set of 67 Nepali characters required for transcription of Nepali speech to text.

/pdf/nepali-speech-recognition-using-rnn-ctc-model-1f864cbme5.pdf

Nepali Speech Recognition using RNN-CTC Model

A biological system is a complex network of heterogeneous molecular entities and their interactions contributing to various biological characteristics of the system. However, current biological networks are noisy, sparse, and incomplete, limiting our ability to create a holistic view of the biological system and understand the biological phenomena. Experimental identification of such interactions is both time-consuming and expensive. With the recent advancements in high-throughput data generation and significant improvement in computational power, various computational methods have been developed to predict novel interactions in the noisy network. Recently, deep learning methods such as graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction. However, graph neural networks-based methods require human expertise and experimentation to design the appropriate complexity of the model and significantly impact the performance of the model. Furthermore, deep graph neural networks face overfitting problems and tend to be poorly calibrated with high confidence on incorrect predictions. To address these challenges, we propose Bayesian model selection for graph convolutional networks to jointly infer the most plausible number of graph convolution layers (depth) warranted by data and perform dropout regularization simultaneously. Experiments on four interaction datasets show that our proposed method achieves accurate and calibrated predictions. Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.

Paribesh Regmi

Papers

Nepali Speech Recognition using RNN-CTC Model

Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks