Automatic Speech Recognition of Continuous Speech Signal of Gujarati Language Using Machine Learning

2021 ◽  
pp. 147-159
Author(s):  
Purnima Pandit ◽  
Priyank Makwana ◽  
Shardav Bhatt
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Signal ◽  
Continuous Speech ◽  
Gujarati Language

scholarly journals Automatic Speech Recognition with Stuttering Speech Removal using Long Short-Term Memory (LSTM)

2020 ◽  
Vol 8 (5) ◽  
pp. 1677-1681
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Signal ◽  
Short Term Memory ◽  
Long Short Term Memory ◽  
Increase In Accuracy ◽  
Two Stages ◽  
The Given ◽  
Asr System

Stuttering or Stammering is a speech defect within which sounds, syllables, or words are rehashed or delayed, disrupting the traditional flow of speech. Stuttering can make it hard to speak with other individuals, which regularly have an effect on an individual's quality of life. Automatic Speech Recognition (ASR) system is a technology that converts audio speech signal into corresponding text. Presently ASR systems play a major role in controlling or providing inputs to the various applications. Such an ASR system and Machine Translation Application suffers a lot due to stuttering (speech dysfluency). Dysfluencies will affect the phrase consciousness accuracy of an ASR, with the aid of increasing word addition, substitution and dismissal rates. In this work we focused on detecting and removing the prolongation, silent pauses and repetition to generate proper text sequence for the given stuttered speech signal. The stuttered speech recognition consists of two stages namely classification using LSTM and testing in ASR. The major phases of classification system are Re-sampling, Segmentation, Pre-Emphasis, Epoch Extraction and Classification. The current work is carried out in UCLASS Stuttering dataset using MATLAB with 4% to 6% increase in accuracy when compare with ANN and SVM.

Introduction to Speech Recognition

2011 ◽  
pp. 141-161
Author(s):  
Sergio Suárez-Guerra ◽  
Jose Luis Oropeza-Rodriguez
Keyword(s):  
Artificial Intelligence ◽  
Signal Processing ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Signal ◽  
Essential Information ◽  
Science Field ◽  
Applied Artificial Intelligence ◽  
And Linguistics ◽  
Successful Technology

This chapter presents the state-of-the-art automatic speech recognition (ASR) technology, which is a very successful technology in the computer science field, related to multiple disciplines such as the signal processing and analysis, mathematical statistics, applied artificial intelligence and linguistics, and so forth. The unit of essential information used to characterize the speech signal in the most widely used ASR systems is the phoneme. However, recently several researchers have questioned this representation and demonstrated the limitations of the phonemes, suggesting that ASR with better performance can be developed replacing the phoneme by triphones and syllables as the unit of essential information used to characterize the speech signal. This chapter presents an overview of the most successful techniques used in ASR systems together with some recently proposed ASR systems that intend to improve the characteristics of conventional ASR systems.

Introduction to Speech Recognition

2011 ◽  
pp. 325-348 ◽  
Author(s):  
Sergio Suárez-Guerra ◽  
Jose Luis Oropeza-Rodriguez
Keyword(s):  
Artificial Intelligence ◽  
Signal Processing ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Signal ◽  
Essential Information ◽  
Science Field ◽  
Applied Artificial Intelligence ◽  
And Linguistics ◽  
Successful Technology

This chapter presents the state-of-the-art automatic speech recognition (ASR) technology, which is a very successful technology in the computer science field, related to multiple disciplines such as the signal processing and analysis, mathematical statistics, applied artificial intelligence and linguistics, and so forth. The unit of essential information used to characterize the speech signal in the most widely used ASR systems is the phoneme. However, recently several researchers have questioned this representation and demonstrated the limitations of the phonemes, suggesting that ASR with better performance can be developed replacing the phoneme by triphones and syllables as the unit of essential information used to characterize the speech signal. This chapter presents an overview of the most successful techniques used in ASR systems together with some recently proposed ASR systems that intend to improve the characteristics of conventional ASR systems.

Machine Learning in Automatic Speech Recognition: A Survey

2015 ◽  
Vol 32 (4) ◽  
pp. 240-251 ◽  
Author(s):  
Jayashree Padmanabhan ◽  
Melvin Jose Johnson Premkumar
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Automatic Speech Recognition

scholarly journals Automatic speech signal segmentation based on the innovation adaptive filter

2014 ◽  
Vol 24 (2) ◽  
pp. 259-270 ◽  
Author(s):  
Ryszard Makowski ◽  
Robert Hossa
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Adaptive Filter ◽  
Speech Signal ◽  
Detection Efficiency ◽  
Difficult Problem ◽  
Second Order ◽  
Signal Segmentation ◽  
Schur Algorithm ◽  
Starting Point

Abstract Speech segmentation is an essential stage in designing automatic speech recognition systems and one can find several algorithms proposed in the literature. It is a difficult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006), and it is a modified version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefficients of an innovation adaptive filter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics defined on the mel spectrum determined from the reflection coefficients. The paper presents the structure of the algorithm, defines its properties, lists parameter values, describes detection efficiency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

Voice-Based Speaker Identification and Verification

2021 ◽  
pp. 288-316
Author(s):  
Keshav Sinha ◽  
Rasha Subhi Hameed ◽  
Partha Paul ◽  
Karan Pratap Singh
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Signal ◽  
Reference Model ◽  
Speaker Identification ◽  
Recognition System ◽  
Speech Recognition System ◽  
Primary Focus ◽  
Dynamic Time ◽  
Dynamic Time Wrapping

In recent years, the advancement in voice-based authentication leads in the field of numerous forensic voice authentication technology. For verification, the speech reference model is collected from various open-source clusters. In this chapter, the primary focus is on automatic speech recognition (ASR) technique which stores and retrieves the data and processes them in a scalable manner. There are the various conventional techniques for speech recognition such as BWT, SVD, and MFCC, but for automatic speech recognition, the efficiency of these conventional recognition techniques degrade. So, to overcome this problem, the authors propose a speech recognition system using E-SVD, D3-MFCC, and dynamic time wrapping (DTW). The speech signal captures its important qualities while discarding the unimportant and distracting features using D3-MFCC.

scholarly journals Continuous Speech Recognition of Kazakh Language

2019 ◽  
Vol 24 ◽  
pp. 01012 ◽  
Author(s):  
Оrken Mamyrbayev ◽  
Mussa Turdalyuly ◽  
Nurbapa Mekebayev ◽  
Kuralay Mukhsina ◽  
Alimukhan Keylan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Error Rate ◽  
Speech Signal ◽  
Deep Neural Networks ◽  
Continuous Speech ◽  
Continuous Speech Recognition ◽  
Reliable System ◽  
Word Error Rate ◽  
Language Studies

This article describes the methods of creating a system of recognizing the continuous speech of Kazakh language. Studies on recognition of Kazakh speech in comparison with other languages began relatively recently, that is after obtaining independence of the country, and belongs to low resource languages. A large amount of data is required to create a reliable system and evaluate it accurately. A database has been created for the Kazakh language, consisting of a speech signal and corresponding transcriptions. The continuous speech has been composed of 200 speakers of different genders and ages, and the pronunciation vocabulary of the selected language. Traditional models and deep neural networks have been used to train the system. As a result, a word error rate (WER) of 30.01% has been obtained.

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