Estimation of ASR Parameterization for Interactive System

2021 ◽  
Vol 10 (1) ◽  
pp. 28-40
Author(s):  
Mohamed Hamidi ◽  
Hassan Satori ◽  
Ouissam Zealouk ◽  
Naouar Laaidi
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Interactive Voice Response ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Interactive System ◽  
Interactive Applications ◽  
Speaker Independent ◽  
Spectral Coefficients

In this study, the authors explore the integration of speaker-independent automatic Amazigh speech recognition technology into interactive applications to extract data remotely from a distance database. Based on the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies, the authors built an interactive speech system to allow users to interact with the interactive system through voice commands. The hidden Markov models (HMMs), Gaussian mixture models (GMMs), and Mel frequency spectral coefficients (MFCCs) are used to develop a speech system based on the ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.

scholarly journals Automatic Speech Recognition (ASR) System for Isolated Marathi Words: using HTK

2019 ◽  
Vol 8 (12) ◽  
pp. 3702-3705
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Viterbi Algorithm ◽  
Gaussian Mixture ◽  
Speech Corpus ◽  
Word Level ◽  
Speaker Independent ◽  
Token Passing ◽  
Mel Frequency Cepstral Coefficient ◽  
Asr System

The present manuscript focuses on building automatic speech recognition (ASR) system for Marathi language (M-ASR) using Hidden Markov Model Toolkit (HTK). The M-ASR system gives the detail about experimentation and implementation using the HTK Toolkit. In this work total 106 speaker independent Marathi isolated words were recognized. These unique Marathi words are used to train and evaluate M-ASR system. The speech corpus (database) is created by us using isolated Marathi words uttered with mixed gender people. The system uses Mel Frequency Cepstral Coefficient (MFCC) for the purpose of extracting features using Gaussian mixture model (GMM). Viterbi algorithm based on token passing is used for decoding to recognize unknown utterances. The proposed M-ASR system is speaker independent. The proposed system has reported 96.23% word level recognition accuracy.

Noise Robust Speech Recognition Using Deep Belief Networks

2016 ◽  
Vol 15 (01) ◽  
pp. 1650005 ◽  
Author(s):  
Mahboubeh Farahat ◽  
Ramin Halavati
Keyword(s):  
Speech Recognition ◽  
Word Recognition ◽  
Markov Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Mel Frequency Cepstral Coefficients ◽  
Recognition Systems ◽  
Low Dimensional

Most current speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. In these systems acoustic inputs are represented by Mel Frequency Cepstral Coefficients temporal spectrogram known as frames. But MFCC is not robust to noise. Consequently, with different train and test conditions the accuracy of speech recognition systems decreases. On the other hand, using MFCCs of larger window of frames in GMMs needs more computational power. In this paper, Deep Belief Networks (DBNs) are used to extract discriminative information from larger window of frames. Nonlinear transformations lead to high-order and low-dimensional features which are robust to variation of input speech. Multiple speaker isolated word recognition tasks with 100 and 200 words in clean and noisy environments has been used to test this method. The experimental results indicate that this new method of feature encoding result in much better word recognition accuracy.

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 />

Literacy by Way of Automatic Speech Recognition

2011 ◽  
pp. 2074-2118
Author(s):  
Russell Gluck ◽  
John Fulcher
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Warping ◽  
Oral Storytelling ◽  
Pattern Recognition Techniques ◽  
Dynamic Time ◽  
Over Time

The chapter commences with an overview of automatic speech recognition (ASR), which covers not only the de facto standard approach of hidden Markov models (HMMs), but also the tried-and-proven techniques of dynamic time warping and artificial neural networks (ANNs). The coverage then switches to Gluck’s (2004) draw-talk-write (DTW) process, developed over the past two decades to assist non-text literate people become gradually literate over time through telling and/or drawing their own stories. DTW has proved especially effective with “illiterate” people from strong oral, storytelling traditions. The chapter concludes by relating attempts to date in automating the DTW process using ANN-based pattern recognition techniques on an Apple Macintosh G4™ platform.

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