Analysis of Complex Non-Linear Environment Exploration in Speech Recognition by Hybrid Learning Technique

Recently, the application of voice-controlled interfaces plays a major role in many real-time environments such as a car, smart home and mobile phones. In signal processing, the accuracy of speech recognition remains a thought-provoking challenge. The filter designs assist speech recognition systems in terms of improving accuracy by parameter tuning. This task is some degree of form filter’s narrowed specifications which lead to complex nonlinear problems in speech recognition. This research aims to provide analysis on complex nonlinear environment and exploration with recent techniques in the combination of statistical-based design and Support Vector Machine (SVM) based learning techniques. Dynamic Bayes network is a dominant technique related to speech processing characterizing stack co-occurrences. This method is derived from mathematical and statistical formalism. It is also used to predict the word sequences along with the posterior probability method with the help of phonetic word unit recognition. This research involves the complexities of signal processing that it is possible to combine sentences with various types of noises at different signal-to-noise ratios (SNR) along with the measure of comparison between the two techniques.

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Speech Processing for Digital Home Assistants: Combining Signal Processing With Deep-Learning Techniques

IEEE Signal Processing Magazine ◽

10.1109/msp.2019.2918706 ◽

2019 ◽

Vol 36 (6) ◽

pp. 111-124 ◽

Cited By ~ 13

Author(s):

Reinhold Haeb-Umbach ◽

Shinji Watanabe ◽

Tomohiro Nakatani ◽

Michiel Bacchiani ◽

Bjorn Hoffmeister ◽

...

Keyword(s):

Signal Processing ◽

Deep Learning ◽

Speech Processing ◽

Digital Home ◽

Learning Techniques

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IMPLEMENTATION OF SPEECH RECOGNITION SYSTEM USING DSP PROCESSOR ADSP2181

International Journal of Electronics Signals and Systems ◽

10.47893/ijess.2012.1056 ◽

2012 ◽

pp. 1-6

Author(s):

KALPANA JOSHI ◽

NILIMA KOLHARE ◽

V.M. PANDHARIPANDE

Keyword(s):

Signal Processing ◽

Speech Recognition ◽

Digital Signal Processing ◽

Speech Processing ◽

Speaker Recognition ◽

Digital Signal ◽

Recognition System ◽

Theory And Practice ◽

Clock Frequency ◽

Application Development

While many Automatic Speech Recognition applications employ powerful computers to handle the complex recognition algorithms, there is a clear demand for effective solutions on embedded platforms. Digital Signal Processing (DSP) is one of the most commonly used hardware platform that provides good development flexibility and requires relatively short application development cycle.DSP techniques have been at the heart of progress in Speech Processing during the last 25years.Simultaneously speech processing has been an important catalyst for the development of DSP theory and practice. Today DSP methods are used in speech analysis, synthesis, coding, recognition, enhancement as well as voice modification, speaker recognition, language identification.Speech recognition is generally computationally-intensive task and includes many of digital signal processing algorithms. In real-time and real environment speech recognisers applications, it’s often necessary to use embedded resource-limited hardware. Less memory, clock frequency, space and cost related to common architecture PC (x86), must be balanced by more effective computation.

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Visual Speech Processing and Recognition

Visual Speech Recognition ◽

10.4018/978-1-60566-186-5.ch009 ◽

2009 ◽

pp. 261-293

Author(s):

Constantine Kotropoulos ◽

Ioannis Pitas

Keyword(s):

Speech Recognition ◽

Support Vector Machines ◽

Speech Processing ◽

Visual Speech ◽

Support Vector ◽

Contour Extraction ◽

Mouth Region ◽

Visual Speech Recognition ◽

Vector Machines ◽

High Level

This chapter addresses both low- and high-level problems in visual speech processing and recognition In particular, mouth region segmentation and lip contour extraction are addressed first. Next, visual speech recognition with parallel support vector machines and temporal Viterbi lattices is demonstrated on a small vocabulary task.

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Incorporating Noise Robustness in Speech Command Recognition by Noise Augmentation of Training Data

Sensors ◽

10.3390/s20082326 ◽

2020 ◽

Vol 20 (8) ◽

pp. 2326

Author(s):

Ayesha Pervaiz ◽

Fawad Hussain ◽

Huma Israr ◽

Muhammad Ali Tahir ◽

Fawad Riasat Raja ◽

...

Keyword(s):

Machine Learning ◽

Speech Recognition ◽

Speech Processing ◽

Recognition System ◽

Training Data ◽

Machine Learning Techniques ◽

Noise Robustness ◽

Human Computer Interfaces ◽

Learning Techniques ◽

Speech Corpora

The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark.

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Speech Processing for Language Learning: A Practical Approach to Computer-Assisted Pronunciation Teaching

Electronics ◽

10.3390/electronics10030235 ◽

2021 ◽

Vol 10 (3) ◽

pp. 235

Author(s):

Natalia Bogach ◽

Elena Boitsova ◽

Sergey Chernonog ◽

Anton Lamtev ◽

Maria Lesnichaya ◽

...

Keyword(s):

Signal Processing ◽

Speech Recognition ◽

Language Learning ◽

Automatic Speech Recognition ◽

Speech Processing ◽

Foreign Language Learning ◽

Performance Estimation ◽

Third Party ◽

Computer Assisted ◽

Speech Transcription

This article contributes to the discourse on how contemporary computer and information technology may help in improving foreign language learning not only by supporting better and more flexible workflow and digitizing study materials but also through creating completely new use cases made possible by technological improvements in signal processing algorithms. We discuss an approach and propose a holistic solution to teaching the phonological phenomena which are crucial for correct pronunciation, such as the phonemes; the energy and duration of syllables and pauses, which construct the phrasal rhythm; and the tone movement within an utterance, i.e., the phrasal intonation. The working prototype of StudyIntonation Computer-Assisted Pronunciation Training (CAPT) system is a tool for mobile devices, which offers a set of tasks based on a “listen and repeat” approach and gives the audio-visual feedback in real time. The present work summarizes the efforts taken to enrich the current version of this CAPT tool with two new functions: the phonetic transcription and rhythmic patterns of model and learner speech. Both are designed on a base of a third-party automatic speech recognition (ASR) library Kaldi, which was incorporated inside StudyIntonation signal processing software core. We also examine the scope of automatic speech recognition applicability within the CAPT system workflow and evaluate the Levenstein distance between the transcription made by human experts and that obtained automatically in our code. We developed an algorithm of rhythm reconstruction using acoustic and language ASR models. It is also shown that even having sufficiently correct production of phonemes, the learners do not produce a correct phrasal rhythm and intonation, and therefore, the joint training of sounds, rhythm and intonation within a single learning environment is beneficial. To mitigate the recording imperfections voice activity detection (VAD) is applied to all the speech records processed. The try-outs showed that StudyIntonation can create transcriptions and process rhythmic patterns, but some specific problems with connected speech transcription were detected. The learners feedback in the sense of pronunciation assessment was also updated and a conventional mechanism based on dynamic time warping (DTW) was combined with cross-recurrence quantification analysis (CRQA) approach, which resulted in a better discriminating ability. The CRQA metrics combined with those of DTW were shown to add to the accuracy of learner performance estimation. The major implications for computer-assisted English pronunciation teaching are discussed.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0177 ◽

2017 ◽

Vol 7 (7) ◽

pp. 172

Author(s):

Anantvir Singh Romana

Keyword(s):

Machine Learning ◽

Subsequent Treatment ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Disease Prediction ◽

Classification Problems ◽

Learning Techniques ◽

Neural Network Classifiers ◽

Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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