Improved Speech Command Classification System for Sinhala Language based on Automatic Speech Recognition

2020 ◽  
pp. 2050009
Author(s):  
Lakshika Kavmini ◽  
Thilini Dinushika ◽  
Uthayasanker Thayasivam ◽  
Sanath Jayasena
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Error Rate ◽  
Classification System ◽  
Value Added ◽  
Gaussian Mixture ◽  
Conversational Agents ◽  
Language Resources ◽  
Dialog Systems ◽  
The Individual

The recent advancements in conversational Artificial Intelligence (AI) are fastly getting integrated with every realm of human lives. Conversational agents who can learn, understand human languages and mimic the human thinking process have already created a revolution in human lifestyle. Understanding the intention of a speaker from his natural speech is a significant step in conversational AI. A major challenge that hinders the efficacy of this process is the lack of language resources. In this research, we address this issue and develop a domain-specific speech command classification system for the Sinhala language, one of the low-resourced languages. An effective speech command classification system can be utilized in several value added applications such as speech dialog systems. Our speech command classification system is developed by integrating Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). The ASR engine is implemented using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and it converts a Sinhala speech command into a corresponding text representation. The text classifier, which is implemented as an ensemble unit of several classifiers, predicts the intent of the speaker when provided with the above text output. In this paper, we discuss and evaluate various algorithms and techniques that can be utilized to optimize the performance of both the ASR and text classifier. As well, we present our novel Sinhala speech data corpus of 4.15[Formula: see text]h which is based on the banking domain. As the final outcome, our system reports its Sinhala speech command classification accuracy as 91.03%. It shows that our system outperforms the state-of-the-art speech-to-intent mapping systems developed for the Sinhala language. The individual evaluation on the ASR system reports a 9.91% Word Error Rate and a 19.95% Sentence Error Rate, suggesting the applicability of advanced speech recognition techniques despite the limited language resources. Finally, our findings deliver useful insights on further research in speech command classification in the low-resourced context.

scholarly journals UCSY-SC1: A Myanmar speech corpus for automatic speech recognition

2019 ◽  
Vol 9 (4) ◽  
pp. 3194 ◽  
Author(s):  
Aye Nyein Mon ◽  
Win Pa Pa ◽  
Ye Kyaw Thu
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Gaussian Mixture ◽  
Error Rates ◽  
Training Data ◽  
Speech Corpus ◽  
Total Size ◽  
Test Sets ◽  
Web News

This paper introduces a speech corpus which is developed for Myanmar Automatic Speech Recognition (ASR) research. Automatic Speech Recognition (ASR) research has been conducted by the researchers around the world to improve their language technologies. Speech corpora are important in developing the ASR and the creation of the corpora is necessary especially for low-resourced languages. Myanmar language can be regarded as a low-resourced language because of lack of pre-created resources for speech processing research. In this work, a speech corpus named UCSY-SC1 (University of Computer Studies Yangon - Speech Corpus1) is created for Myanmar ASR research. The corpus consists of two types of domain: news and daily conversations. The total size of the speech corpus is over 42 hrs. There are 25 hrs of web news and 17 hrs of conversational recorded data.<br />The corpus was collected from 177 females and 84 males for the news data and 42 females and 4 males for conversational domain. This corpus was used as training data for developing Myanmar ASR. Three different types of acoustic models  such as Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models were built and compared their results. Experiments were conducted on different data  sizes and evaluation is done by two test sets: TestSet1, web news and TestSet2, recorded conversational data. It showed that the performance of Myanmar ASRs using this corpus gave satisfiable results on both test sets. The Myanmar ASR  using this corpus leading to word error rates of 15.61% on TestSet1 and 24.43% on TestSet2.<br /><br />

scholarly journals Automatic Speech Recognition in Mobile Customer Care Service

2015 ◽  
Vol 4 (01) ◽  
pp. 07-11
Author(s):  
Ms. Pooja Sahu
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Care Service ◽  
Recognition System ◽  
Care Services ◽  
Mobile Shopping ◽  
Customer Care ◽  
The One ◽  
The Individual ◽  
The Voice

In the project an automatic speech system is used in mobile customer care   services. In existing  mobile  customer care  services, customer  have  to  wait for 4 to 5 minutes  to get  into the  option  what   they  want to  inquire. Based on the requirement, we go for filtering the incoming calls. Persons who require particular data are dynamically move to speech recognition system that identifies the type of the enquiry chosen. Speech recognition is the one which dynamically identifies the individual speaking based on analyzing the speech waves. It helps in identifying the voice of the speaker to know the recognized user. It also helps in accessing services like telephone banking, mobile shopping, database services and securing the information which is confidential.

scholarly journals Indigenuous Vocabulary Reformulation for Continuousyorùbá Speech Recognition In M-Commerce Using Acoustic Nudging-Based Gaussian Mixture Model

2021 ◽  
Author(s):  
Kehinde Lydia Ajayi ◽  
Victor Azeta ◽  
Isaac Odun-Ayo ◽  
Ambrose Azeta ◽  
Ajayi Peter Taiwo ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Error Rate ◽  
System Performance ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Computer Applications ◽  
Word Error Rate ◽  
The Mean

Abstract One of the current research areas is speech recognition by aiding in the recognition of speech signals through computer applications. In this research paper, Acoustic Nudging, (AN) Model is used in re-formulating the persistence automatic speech recognition (ASR) errors that involves user’s acoustic irrational behavior which alters speech recognition accuracy. GMM helped in addressing low-resourced attribute of Yorùbá language to achieve better accuracy and system performance. From the simulated results given, it is observed that proposed Acoustic Nudging-based Gaussian Mixture Model (ANGM) improves accuracy and system performance which is evaluated based on Word Recognition Rate (WRR) and Word Error Rate (WER)given by validation accuracy, testing accuracy, and training accuracy. The evaluation results for the mean WRR accuracy achieved for the ANGM model is 95.277% and the mean Word Error Rate (WER) is 4.723%when compared to existing models. This approach thereby reduce error rate by 1.1%, 0.5%, 0.8%, 0.3%, and 1.4% when compared with other models. Therefore this work was able to discover a foundation for advancing current understanding of under-resourced languages and at the same time, development of accurate and precise model for speech recognition.

scholarly journals Feature compensation based on the normalization of vocal tract length for the improvement of emotion-affected speech recognition

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Masoud Geravanchizadeh ◽  
Elnaz Forouhandeh ◽  
Meysam Bashirpour
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Vocal Tract ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Speech Recognition System ◽  
Emotional States ◽  
Emotional Speech ◽  
Automatic Speech Recognition System ◽  
Frequency Warping

AbstractThe performance of speech recognition systems trained with neutral utterances degrades significantly when these systems are tested with emotional speech. Since everybody can speak emotionally in the real-world environment, it is necessary to take account of the emotional states of speech in the performance of the automatic speech recognition system. Limited works have been performed in the field of emotion-affected speech recognition and so far, most of the researches have focused on the classification of speech emotions. In this paper, the vocal tract length normalization method is employed to enhance the robustness of the emotion-affected speech recognition system. For this purpose, two structures of the speech recognition system based on hybrids of hidden Markov model with Gaussian mixture model and deep neural network are used. To achieve this goal, frequency warping is applied to the filterbank and/or discrete-cosine transform domain(s) in the feature extraction process of the automatic speech recognition system. The warping process is conducted in a way to normalize the emotional feature components and make them close to their corresponding neutral feature components. The performance of the proposed system is evaluated in neutrally trained/emotionally tested conditions for different speech features and emotional states (i.e., Anger, Disgust, Fear, Happy, and Sad). In this system, frequency warping is employed for different acoustical features. The constructed emotion-affected speech recognition system is based on the Kaldi automatic speech recognition with the Persian emotional speech database and the crowd-sourced emotional multi-modal actors dataset as the input corpora. The experimental simulations reveal that, in general, the warped emotional features result in better performance of the emotion-affected speech recognition system as compared with their unwarped counterparts. Also, it can be seen that the performance of the speech recognition using the deep neural network-hidden Markov model outperforms the system employing the hybrid with the Gaussian mixture model.

Performance Analysis of various Front-end and Back End Amalgamations for Noise-robust DNN-based ASR

2020 ◽  
Vol 13 ◽  
Author(s):  
Mohit Dua ◽  
Pawandeep Singh Sethi ◽  
Vinam Agrawal ◽  
Raghav Chawla
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Gaussian Mixture ◽  
Performance Comparison ◽  
Acoustic Modeling ◽  
Extraction Techniques ◽  
Front End ◽  
Noise Robust ◽  
Asr System

Introduction: An Automatic Speech Recognition (ASR) system enables to recognize the speech utterances and thus can be used to convert speech into text for various purposes. These systems are deployed in different environments such as clean or noisy and are used by all ages or types of people. These also present some of the major difficulties faced in the development of an ASR system. Thus, an ASR system need to be efficient, while also being accurate and robust. Our main goal is to minimize the error rate during training as well as testing phases, while implementing an ASR system. Performance of ASR depends upon different combinations of feature extraction techniques and back-end techniques. In this paper, using a continuous speech recognition system, the performance comparison of different combinations of feature extraction techniques and various types of back-end techniques has been presented Methods: Hidden Markov Models (HMMs), Subspace Gaussian Mixture Models (SGMMs) and Deep Neural Networks (DNNs) with DNN-HMM architecture, namely Karel's, Dan's and Hybrid DNN-SGMM architecture are used at the back-end of the implemented system. Mel frequency Cepstral Coefficient (MFCC), Perceptual Linear Prediction (PLP), and Gammatone Frequency Cepstral coefficients (GFCC) are used as feature extraction techniques at the front-end of the proposed system. Kaldi toolkit has been used for the implementation of the proposed work. The system is trained on the Texas Instruments-Massachusetts Institute of Technology (TIMIT) speech corpus for English language Results: The experimental results show that MFCC outperforms GFCC and PLP in noiseless conditions, while PLP tends to outperform MFCC and GFCC in noisy conditions. Furthermore, the hybrid of Dan's DNN implementation along with SGMM performs the best for the back-end acoustic modeling. The proposed architecture with PLP feature extraction technique in the front end and hybrid of Dan's DNN implementation along with SGMM at the back end outperforms the other combinations in a noisy environment. Conclusion: Automatic Speech recognition has numerous applications in our lives like Home automation, Personal assistant, Robotics etc. It is highly desirable to build an ASR system with good performance. The performance Automatic Speech Recognition is affected by various factors which include vocabulary size, whether system is speaker dependent or independent, whether speech is isolated, discontinuous or continuous, adverse conditions like noise. The paper presented an ensemble architecture that uses PLP for feature extraction at the front end and a hybrid of SGMM + Dan's DNN in the backend to build a noise robust ASR system Discussion: The presented work in this paper discusses the performance comparison of continuous ASR systems developed using different combinations of front-end feature extraction (MFCC, PLP, and GFCC) and back-end acoustic modeling (mono-phone, tri-phone, SGMM, DNN and hybrid DNN-SGMM) techniques. Each type of front-end technique is tested in combination with each type of back-end technique. Finally, it compares the results of the combinations thus formed, to find out the best performing combination in noisy and clean conditions

scholarly journals An Effective Learning Method for Automatic Speech Recognition in Korean CI Patients’ Speech

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 807
Author(s):  
Jiho Jeong ◽  
S. I. M. M. Raton Mondol ◽  
Yeon Wook Kim ◽  
Sangmin Lee
Keyword(s):  
Speech Recognition ◽  
Cochlear Implant ◽  
Automatic Speech Recognition ◽  
Error Rate ◽  
Training Data ◽  
Learning Method ◽  
Test Dataset ◽  
Privacy Concerns ◽  
Effective Learning ◽  
Speech Test

The automatic speech recognition (ASR) model usually requires a large amount of training data to provide better results compared with the ASR models trained with a small amount of training data. It is difficult to apply the ASR model to non-standard speech such as that of cochlear implant (CI) patients, owing to privacy concerns or difficulty of access. In this paper, an effective finetuning and augmentation ASR model is proposed. Experiments compare the character error rate (CER) after training the ASR model with the basic and the proposed method. The proposed method achieved a CER of 36.03% on the CI patient’s speech test dataset using only 2 h and 30 min of training data, which is a 62% improvement over the basic method.

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