A Study of Machine Learning Algorithms in Speech Recognition and Language Identification System

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.

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Semi-Automated Identification of Biomedical Literature: A Proof of Concept Study

10.21203/rs.3.rs-560637/v1 ◽

2021 ◽

Author(s):

Gaelen P. Adam ◽

Dimitris Pappas ◽

Haris Papageorgiou ◽

Evangelos Evangelou ◽

Thomas A. Trikalinos

Keyword(s):

Machine Learning ◽

Domain Knowledge ◽

Contextual Information ◽

Learning Algorithms ◽

Knowledge Bases ◽

Machine Learning Algorithms ◽

Biomedical Literature ◽

Future Research ◽

Identification System ◽

Query Formulation

Abstract Background: The typical approach to literature identification involves two discrete and successive steps: (i) formulating a search strategy (i.e., a set of Boolean queries) and (ii) manually identifying the relevant citations in the corpus returned by the query. We have developed a literature identification system (Pythia) that combines the query formulation and citation screening steps and uses modern approaches for text encoding (dense text embeddings) to represent the text of the citations in a form that can be used by information retrieval and machine learning algorithms.Methods: Pythia incorporates a set of natural-language questions with machine-learning algorithms to rank all PubMed citations based on relevance. Pythia returns the 100 top-ranked citations for all questions combined. These 100 articles are exported, and a human screener adjudicates the relevance of each abstract and tags words that indicate relevance. The “curated” articles are then exploited by Pythia to refine the search and re-rank the abstracts, and a new set of 100 abstracts is exported and screened/tagged, until convergence (i.e., no other relevant abstracts are retrieved) or for a set number of iterations (batches). Pythia performance was assessed using seven systematic reviews (three prospectively and four retrospectively). Sensitivity, precision, and the number needed to read were calculated for each review. Results: The ability of Pythia to identify the relevant articles (sensitivity) varied across reviews from a low of 0.09 for a sleep apnea review to a high of 0.58 for a diverticulitis review. The number of abstracts that a reviewer had to read to find one relevant abstract (NNR) was lower than in the manually screened project in four reviews, higher in two, and had mixed results in one. The reviews that had greater overall sensitivity retrieved more relevant citations in early batches, but neither study design, study size, nor specific key question significantly affected retrieval across all reviews.Conclusions: Future research should explore ways to encode domain knowledge in query formulation, possibly by incorporating a "reasoning" aspect to Pythia to elicit more contextual information and leveraging ontologies and knowledge bases to better enrich the questions used in the search.

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Linear Predictive Coefficients-Based Feature to Identify Top-Seven Spoken Languages

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420580069 ◽

2019 ◽

Vol 34 (06) ◽

pp. 2058006 ◽

Cited By ~ 4

Author(s):

Himadri Mukherjee ◽

Ankita Dhar ◽

Sk. Md. Obaidullah ◽

K. C. Santosh ◽

Santanu Phadikar ◽

...

Keyword(s):

Machine Learning ◽

Speech Recognition ◽

Ensemble Learning ◽

Real World ◽

Language Identification ◽

Identification System ◽

Machine Learning Classifiers ◽

World Languages ◽

The Pacific ◽

Multiple Languages

Speech recognition in multilingual scenario is not trivial in the case when multiple languages are used in one conversation. Language must be identified before we process speech recognition as such tools are language-dependent. We present a language identification system (or AI tool) to distinguish top-seven world languages namely Chinese, Spanish, English, Hindi, Arabic, Bangla and Portuguese [G. F. Simons and C. D. Fennig (eds.), Ethnologue: Laguage of the Americas and the Pacific, Twentieth Edn. (SIL Internatinal, 2017)]. The system uses linear predictive coefficients-based feature, i.e. the line spectral pair–grade ratio (LSP–GR) feature, and ensemble learning for classification. Experiments were performed on more than 200[Formula: see text]h of real-world YouTube data and the highest possible accuracy of 96.95% was received. The results can be compared with other machine learning classifiers.

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Supplemental Material for One Model to Rule Them All? Using Machine Learning Algorithms to Determine the Number of Factors in Exploratory Factor Analysis

Psychological Methods ◽

10.1037/met0000262.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Factor Analysis ◽

Exploratory Factor Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Number Of Factors

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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The Unlearnable Checkerboard Pattern

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.1.2.holloway.1 ◽

2019 ◽

Vol 1 (2) ◽

pp. 78-80

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Checkerboard Pattern ◽

Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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