Speech recognition thesis report

In the second part, the automatic generation of lexica for a phoneme based recogniser was studied. Another reason why HMMs are popular is because they can be trained automatically and are simple and computationally feasible to use.

Some of the most recent[ when? Deep learning A deep feedforward neural network DNN is an artificial neural network with multiple hidden layers of units between the input and output layers. Dynamic time warping is an algorithm for measuring similarity between two sequences that may vary in time or speed.

Speech recognition

The loss function is usually the Levenshtein distancethough it can be different distances for specific tasks; the set of possible transcriptions is, of course, pruned to maintain tractability. Re scoring is usually done by trying Speech recognition thesis report minimize the Bayes risk [57] or an approximation thereof: Dynamic time warping Dynamic time warping is an approach that was historically used for speech recognition but has now largely been displaced by the more successful HMM-based approach.

Huang went on to found the speech recognition group at Microsoft in The subband processing is done using relatively short fixed FIR filters. Further reductions in word error rate came as researchers shifted acoustic models to be discriminative instead of using maximum likelihood estimation.

The set of candidates can be kept either as a list the N-best list approach or as a subset of the models a lattice. The effectiveness of the product is the problem that is hindering it being effective.

An important exception is the lexicon that is required in a sub-word based recogniser to build word models from the models of basic recognition units. Jointly, the RNN-CTC model learns the pronunciation and acoustic model together, however it is incapable of learning the language due to conditional independence assumptions similar to a HMM.

Appendix A contains the user manuals for the different programs that were written as a part of this thesis work. Speech recognition results showed that the performance obtained with the CIS strategy was not statistically different with the performance obtained with the PPS, QPS, the hybrid strategies in quiet, and with the 6-of-8 strategy in noise.

Much remains to be done both in speech recognition and in overall speech technology in order to consistently achieve performance improvements in operational settings. Numerous approaches have been proposed for speech enhancement, with the spectral subtraction method being one of the most popular, due to its relatively simple implementation and computational efficiency.

Further enhancements in speech quality were obtained by applying a perceptual weighting function estimated using a psychoacoustics model that was designed to minimize noise distortion. Some government research programs focused on intelligence applications of speech recognition, e.

This means, during deployment, there is no need to carry around a language model making it very practical for deployment onto applications with limited memory. Raj Reddy was the first person to take on continuous speech recognition as a graduate student at Stanford University in the late s.

Although DTW would be superseded by later algorithms, the technique of dividing the signal into frames would carry on. For example, a n-gram language model is required for all HMM-based systems, and a typical n-gram language model often takes several gigabytes in memory making them impractical to deploy on mobile devices.

Many systems use so-called discriminative training techniques that dispense with a purely statistical approach to HMM parameter estimation and instead optimize some classification-related measure of the training data.

Due to the inability of feedforward Neural Networks to model temporal dependencies, an alternative approach is to use neural networks as a pre-processing e. Wideband active noise control systems often involve adaptive filter lengths with hundreds of taps.

This principle was first explored successfully in the architecture of deep autoencoder on the "raw" spectrogram or linear filter-bank features, [74] showing its superiority over the Mel-Cepstral features which contain a few stages of fixed transformation from spectrograms.

Back-end or deferred speech recognition is where the provider dictates into a digital dictation system, the voice is routed through a speech-recognition machine and the recognized draft document is routed along with the original voice file to the editor, where the draft is edited and report finalized.

The true "raw" features of speech, waveforms, have more recently been shown to produce excellent larger-scale speech recognition results.

Handling continuous speech with a large vocabulary was a major milestone in the history of speech recognition.

N-Best Search Methods Applied to Speech Recognition

They can also utilize speech recognition technology to freely enjoy searching the Internet or using a computer at home without having to physically operate a mouse and keyboard.

By contrast, many highly customized systems for radiology or pathology dictation implement voice "macros", where the use of certain phrases — e. Individuals with learning disabilities who have problems with thought-to-paper communication essentially they think of an idea but it is processed incorrectly causing it to end up differently on paper can possibly benefit from the software but the technology is not bug proof.

August An understanding of how information about the speech signal is spread among the various frequency bands of the spectrum is essential in numerous communications, audio and hearing related applications.

Speech recognition and synthesis techniques offer the potential to eliminate the need for a person to act as pseudo-pilot, thus reducing training and support personnel. Speech can be thought of as a Markov model for many stochastic purposes. They will be described and compared in this report. In this way, also complex knowledge sources can easily be included in the recognition process.

Early work[ edit ] In three Bell Labs researchers, Stephen.In speech recognition phase, the experiment is repeated ten times for each of the above words. The resulting efficiency percentage and its corresponding efficiency chart are shown in table 2 and figure 6 respectively.

This thesis report is organised as follows: In Chapter 2, a short overview of the fundamentals of speech recognition is given. The concept of Hidden Markov Models is reviewed and the HMM Toolkit (HTK) is described.

Speech Recognition MY Final Year Project. For Later. save. Related. Info. Embed. Share. Print. This thesis report considers an overview of speech recognition technology, software development, and its applications.

Later part of report covers the speech recognition process, and the code for the software and its working. Finally the /5(37). Thesis Report: Supervisor: Prof Mumit Khan Conducted by: Shammur Absar Chowdhury Speech recognition and understanding of spontaneous speech have been a goal of research since It is a process of conversion.

Speech Recognition Using Connectionist Networks Dissertation Proposal Abstract The thesis of the proposed research is that connectionist networks are adequate models. Speech Recognition (CSR) system but preferably in our mother tongue –Bangla.

It is an area where a lot to contribute for our language to establish in computer field.

Speech recognition thesis report
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