IndexIntroductionMethodologyAutomatic speech recognition (ASR)Experiment and resultConclusion and discussionIntroductionSpeech recognition is the ability of a machine, a database or a program to recognize sentences, expressions or words in the spoken language and translate them into a machine-readable layout. Identifying the dialogue of non-native speakers is, in itself, a very challenging task. The speech recognition conversation has been around for years, but the question you should ask yourself should be why it is now relevant. The reasoning is that deep learning has finally made speech recognition accurate enough to be useful outside of a carefully controlled environment. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Machine learning is the realization that there are universal algorithms that can say something fascinating and captivating about a set of data without you having to write any custom code specific to the problem. Instead of writing codes, data is fed into the generic algorithm and it builds its logic based on the data. Suffice it to say, with the current layout of the world, however, the variety of users underlines serious considerations. However, the prerogative that all users should be given the same access to speech recognition is not solid. This is synonymous with the analogy that people with poor reading skills do not have the same access to newspapers as largely literate people. In simple terms, machine learning is a term that hides different types of generic algorithms. Furthermore, non-native recognition serves a critical need for border control security systems. Such recognition systems help security officials identify immigrants with forged and falsified permits or identity documents by identifying the actual country in which an articulate foreign accent is expressed. Furthermore, it appears that speech recognition applications are on track to become a default interface for information delivery systems. Housing users whose language use is impaired in some way is not only a research problem but also a useful concern of great importance. Methodology There are a few considerations, however, that seem primarily useful for non-native language coding. Modality, choice, lexical and syntactic strength, accent, and fluency are aspects of spoken English that can both label disparity in the native language and be used to differentiate it from native speakers. “The accent commonly derives from the habit of articulation of the speaker in his language.” Since it is widely known that beginners of a language are mostly exposed to preliminary grammar at the early stage of their study, however, imperfect mastery of syntax is one of the types that can make even very advanced speech non-native. Lately, the perception between native and non-native language has been tortured by means of binary classification structures. These structures essentially depend on prosodic, cepstral, speech recognition-based or N-gram types of language and use support vector machines (SVMs) for classification. Automatic Speech Recognition (ASR) Efforts to build automatic speech recognition (ASR) systems were first made in the 1950s. These early speech recognition systems attempted to relate a set of grammatical and syntactic rules to identify speech. The system couldidentify the word only if the words spoken follow a certain set of rules. An automatic speech recognition (ASR) module forms the basis of virtually all spoken language evaluation systems. An ASR front-end component for most state-of-the-art rating systems provides speculation about the responses provided by the person available for the rating. As a result, it can be expected that to train this type of ASR module would require a huge amount of data, more precisely a pool of non-native speech and careful transcriptions of every part of that speech. Furthermore, there is little doubt that this would involve a human effort in transcribing the entire collection of speeches. Despite the progress of automatic speech recognition (ASR) systems that have led to advocates, it is still a challenge to develop robust ASR systems that offer high performance to different user groups. The problem with current ASR systems is that they work mainly with native speech only, and the correctness decreases affectedly when words are articulated with an unusual pronunciation (foreign accent). However, human language has numerous concessions to its guidelines. The way words and sentences are articulated can be greatly altered by dialects, assents and mannerisms. First, there is a disparity in what is said by the speaker. For open vocabulary systems, there is no way to collect training data for every imaginable expression or even every possible word. Second, there is dissimilarity due to differences between speakers. Different people have different voices, accents and ways of speaking. Third, there is variation in noise conditions. Anything in the acoustic data other than the signal is noise, and so noise can include background sounds, microphone-specific artifacts, and other effects. Therefore, to realize automatic speech recognition, we use deep learning algorithm. Therefore, for this study, Deep Learning algorithm will be considered as our methodology. You might also be interested to know that deep learning touts who know next to nothing about language translation are putting together relatively simple machine learning solutions that are destroying the best expert-built language translation systems in our world today. Experiment and result In machine learning, a neural system The network (Deep learning) is a construction used mainly for clustering or regression tasks when the extraordinary dimensionality and non-linearity of the data make such tasks unlikely to be completed. In the case of visual data, the standard is to use convolutional neural networks (CNN). CNNs are directly inspired by the cell hierarchy in visual neuroscience. It is important to note that the neural network itself is not an algorithm, but rather a card for many other machine learning algorithms to work collectively and process multiple and complicated data inputs. Siniscalchi et al. , (2013) have already established that the mode and place of articulation attributes can efficiently characterize any spoken language along the same lines as the Automatic Speech Attribute Transcription (ASAT) model for automatic speech recognition. Please note: this is just an example. Get a custom paper now from our expert writers. Get a Custom Essay Conclusion and Discussion Non-native human language is multifaceted; therefore, it limits many studies in their research to a selected speaking group or nation. Scheduled assessment of some aspects of spoken language ability, including grammar,..