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Speech recognition (in many contexts also known as automatic speech recognition, computer speech recognition or erroneously as voice recognition) is the process of converting a speech signal to a sequence of words in the form of digital data, by means of an algorithm implemented as a computer program.Speech recognition applications that have emerged over the last few years include voice dialing (e.g., "Call home"), call routing (e.g., "I would like to make a collect call"), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g., a radiology report), domotic appliance control and content-based spoken audio search (e.g. find a podcast where particular words were spoken).

Voice recognition or speaker recognition is a related process that attempts to identify the person speaking, as opposed to what is being said.

History According to industry experts, at its inception, speech recognition (SR) was sold as a way to completely eliminate transcription rather than bring efficiencies to the transcription process, hence it was not accepted, also given the fact that SR at that time was not up to the task technically. Secondly it required physicians to change the way they work and document clinical encounters - many if not all were reluctant to do that. The biggest reason that speech recognition has not been able to reasonably automate transcription is due limitation of the software and the highly interpretive nature of narrative dictation that often requires human judgment that a speech recognition system just cannot provide. Another limitation is the extensive amount of resource time required of the provider to train the software.

Current Even in the wake of improving speech recognition technologies, medical transcriptionists (MTs) are not expected to become obsolete. Many experts in the health care field anticipate that with increased use of speech recognition technology, the services provided will be redistributed rather than replaced. Speech recognition will not make the skills of MTs obsolete.

Speech recognition can be implemented in front-end or back-end of the medical documentation process.

Front-End SR is where the provider dictates into a speech-recognition engine, the recognized words are displayed right after they are spoken, and the dictator is responsible for editing and signing off on the document. It never goes through an MT/editor.

Back-End SR or Deferred SR is where the provider dictates into a digital dictation system, and the voice is routed through a speech-recognition machine and the recognized draft document is routed along with the original voice file to the MT/editor, who edits the draft and finalizes the report. Deferred SR is being widely used in the industry currently.

Many EMR applications can be more effective and easy to use when deployed in conjunction with a speech-recognition engine. Searches, queries, and form filling are all faster to perform by voice than using a keyboard. Performance of speech recognition systems The performance of a speech recognition systems is usually specified in terms of accuracy and speed. Accuracy is measured with the word error rate, whereas speed is measured with the real time factor.

Most speech recognition users would tend to agree that dictation machines can achieve very high performance in controlled conditions. Part of the confusion mainly comes from the mixed usage of the terms "speech recognition" and "dictation".

Speaker-dependent dictation systems requiring a short period of training can capture continuous speech with a large vocabulary at normal pace with a very high accuracy. Most commercial companies claim that recognition software can achieve between 98% to 99% accuracy if operated under optimal conditions. These optimal conditions usually means the test subjects have:

matching speaker characteristics with the training data,
proper speaker adaptation, and
clean environment (e.g. office space).

This explains why some users, especially those whose speech is heavily accented, might actually perceive the recognition rate to be much lower than expected. Speech recognition in video has become a popular search technology used by several video search companies.

Limited vocabulary systems, requiring no training, can recognize a small number of words (for instance, the ten digits) as spoken by most speakers. Such systems are popular for routing incoming phone calls to their destinations in large organizations.

Both acoustic modelling and language modelling are important studies in modern statistical speech recognition. In this entry, we will discuss the use of hidden Markov model (HMM) which is widely used in many systems. (Language modelling has many other applications such as smart keyboard and document classification; to the corresponding entries.)

The Carnegie Mellon University has made some good steps in increasing the speed of speech chips by using Application-specific_integrated_circuits (application-specific integrated circuits) and reconfigurable chips called Fpgas (field programmable gate arrays). {{cite news |url=http://www.axistive.com/computer-chips-to-enhance-speech-recognition.html | last=Dennis van der Heijden| title=Computer Chips to Enhance Speech Recognition| publisher=Axistive.com| date=2003-10-06-->

Hidden Markov model (HMM)-based speech recognition Modern general-purpose speech recognition systems are generally based on Hidden Markov Model. These are statistical models which output a sequence of symbols or quantities.One possible reason why HMMs are used in speech recognition is that a speech signal could be viewed as a piece-wise stationary signal or a short-time stationary signal. That is, one could assume in a short-time in the range of 10 milliseconds, speech could be approximated as a stationary process. Speech could thus be thought as a Markov model for many stochastic processes (known as states).

Another reason why HMMs are popular is because they can be trained automatically and are simple and computationally feasible to use. In speech recognition, to give the very simplest set up possible, the hidden Markov model would output a sequence of n-dimensional real-valued vectors with n around, say, 13, outputting one of these every 10 milliseconds. The vectors, again in the very simplest case, would consist of cepstrum coefficients, which are obtained by taking a Fourier transform of a short-time window of speech and decorrelating the spectrum using a cosine transform, then taking the first (most significant) coefficients. The hidden Markov model will tend to have, in each state, a statistical distribution called a mixture of diagonal covariance Gaussians which will give a likelihood for each observed vector. Each word, or (for more general speech recognition systems), each phoneme, will have a different output distribution; a hidden Markov model for a sequence of words or phonemes is made by concatenating the individual trained hidden Markov models for the separate words and phonemes.

Described above are the core elements of the most common, HMM-based approach to speech recognition. Modern speech recognition systems use various combinations of a number of standard techniques in order to improve results over the basic approach described above. A typical large-vocabulary system would need context dependency for the phones (so phones with different left and right context have different realizations as HMM states); it would use cepstral normalization to normalize for different speaker and recording conditions; for further speaker normalization it might use vocal tract length normalization (VTLN) for male-female normalization and maximum likelihood linear regression (MLLR) for more general speaker adaptation. The features would have so-called delta and delta-delta coefficients to capture speech dynamics and in addition might use heteroscedastic linear discriminant analysis (HLDA); or might skip the delta and delta-delta coefficients and use splicing and an LDA-based projection followed perhaps by heteroscedastic linear discriminant analysis or a global semitied covariance transform (also known as maximum likelihood linear transform, or MLLT). Many systems use so-called discriminative training techniques which dispense with a purely statistical approach to HMM parameter estimation and instead optimize some classification-related measure of the training data. Examples are maximum mutual information (MMI), minimum classification error (MCE) and minimum phone error (MPE).

Decoding of the speech (the term for what happens when the system is presented with a new utterance and must compute the most likely source sentence) would probably use the Viterbi algorithm to find the best path, and here there is a choice between dynamically creating a combination hidden Markov model which includes both the acoustic and language model information, or combining it statically beforehand (the finite state transducer, or FST, approach).

Dynamic time warping (DTW)-based speech recognition Dynamic time warping is an approach that was historically used for speech recognition but has now largely been displacedby the more successful HMM-based approach.Dynamic time warping is an algorithm for measuring similarity between two sequences which may vary in time or speed. For instance, similarities in walking patterns would be detected, even if in one video the person was walking slowly and if in another they were walking more quickly, or even if there were accelerations and decelerations during the course of one observation. DTW has been applied to video, audio, and graphics -- indeed, any data which can be turned into a linear representation can be analyzed with DTW.

A well known application has been automatic speech recognition, to cope with different speaking speeds. In general, it is a method that allows a computer to find an optimal match between two given sequences (e.g. time series) with certain restrictions, i.e. the sequences are "warped" non-linearly to match each other. This sequence alignment method is often used in the context of hidden Markov models.

For further information Popular speech recognition conferences held each year or two include ICASSP, Eurospeech/ICSLP (now named Interspeech) and the IEEE ASRU. Conferences in the field of Natural Language Processing, such as ACL, NAACL, EMNLP, and HLT, are beginning to include papers on speech processing. Important journals include the IEEE Transactions on Speech and Audio Processing (now named IEEE Transactions on Audio, Speech and Language Processing), Computer Speech and Language, and Speech Communication. Books like "Fundamentals of Speech Recognition" by Lawrence Rabiner can be useful to acquire basic knowledge but may not be fully up to date (1993). Another good source can be "Statistical Methods for Speech Recognition" by Frederick Jelinek which is a more up to date book (1998). Even more up to date is "Computer Speech", by Manfred R. Schroeder, second edition published in 2004. A good insight into the techniques used in the best modern systems can be gained by paying attention to government sponsored competitions such as those organised by DARPA (the largest speech recognition-related project ongoing as of 2007 is the GALE project, which involves both speech recognition and translation components).

In terms of freely available resources, the HTK book (and the accompanying HTK toolkit) is one place to start to both learn about speech recognition and to start experimenting. Another such resource is Carnegie Mellon University's SPHINX toolkit.

Applications of speech recognition

Automatic translation
Automotive speech recognition
Speech Biometric Recognition
Dictation
Hands-free computing: voice command recognition computer user interface
Home automation
Interactive voice response
Medical transcription
Mobile telephony
Pronunciation
Robotics