AI News, Mitsubishi Quiets Car Noise With Machine Learning

Mitsubishi Quiets Car Noise With Machine Learning

Mitsubishi Electric is claiming a breakthrough with its development of noise suppression technology to aid hands-free phone callsin the car and elsewhere.

“It is much harder to reduce noise when its characteristics are largely unpredictable.” To better distinguish human speech from other sounds, the researchers are developing speech-enhancement systems that learn to exploit spectral and dynamic characteristics of human speech such as pitch and timber.

So to effectively remove the noise, the filter needs to have a fine frequency resolution and be updated very rapidly.” In tests, Le Roux says they were able to cancel out 96 percent of the ambient noise compared to just 78 percent achieved by conventional methods.

“Our technology will also be useful for hands-free command and control situations, such as when using Apple’s Siri or Google’s Voice Search in smart phones, as well as in call centers that use speech recognition to handle common requests.” Mitsubishi plans to launch the technology in 2018 in its line of automotive navigation and communication devices.

Speech recognition

Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers.

find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g.

Recognizing the speaker can simplify the task of translating speech in systems that have been trained on a specific person's voice or it can be used to authenticate or verify the identity of a speaker as part of a security process.[7]

The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems.

In 1971, DARPA funded five years of speech recognition research through its Speech Understanding Research program with ambitious end goals including a minimum vocabulary size of 1,000 words.

Jelinek's statistical approach put less emphasis on emulating the way the human brain processes and understands speech in favor of using statistical modeling techniques like HMMs.

However, the HMM proved to be a highly useful way for modeling speech and replaced dynamic time warping to become the dominant speech recognition algorithm in the 1980s.[21]

As the technology advanced and computers got faster, researchers began tackling harder problems such as larger vocabularies, speaker independence, noisy environments and conversational speech.

Further reductions in word error rate came as researchers shifted acoustic models to be discriminative instead of using maximum likelihood estimation.[28]

In the mid-Eighties new speech recognition microprocessors were released: for example RIPAC, an independent-speaker recognition (for continuous speech) chip tailored for telephone services, was presented in the Netherlands in 1986.[29]

The use of deep feedforward (non-recurrent) networks for acoustic modeling was introduced during later part of 2009 by Geoffrey Hinton and his students at University of Toronto and by Li Deng and colleagues at Microsoft Research, initially in the collaborative work between Microsoft and University of Toronto which was subsequently expanded to include IBM and Google (hence 'The shared views of four research groups' subtitle in their 2012 review paper).[43][44][45]

Most speech recognition researchers who understood such barriers hence subsequently moved away from neural nets to pursue generative modeling approaches until the recent resurgence of deep learning starting around 2009–2010 that had overcome all these difficulties.

reviewed part of this recent history about how their collaboration with each other and then with colleagues across four groups (University of Toronto, Microsoft, Google, and IBM) ignited a renaissance of applications of deep feedforward neural networks to speech recognition.[44][45][54][55]

In speech recognition, the hidden Markov model would output a sequence of n-dimensional real-valued vectors (with n being a small integer, such as 10), outputting one of these every 10 milliseconds.

The vectors would consist of cepstral 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.

Modern speech recognition systems use various combinations of a number of standard techniques in order to improve results over the basic approach described above.

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.

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 semi-tied co variance transform (also known as maximum likelihood linear transform, or MLLT).

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.

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, and combining it statically beforehand (the finite state transducer, or FST, approach).

possible improvement to decoding is to keep a set of good candidates instead of just keeping the best candidate, and to use a better scoring function (re scoring) to rate these good candidates so that we may pick the best one according to this refined score.

(or an approximation thereof): Instead of taking the source sentence with maximal probability, we try to take the sentence that minimizes the expectancy of a given loss function with regards to all possible transcriptions (i.e., we take the sentence that minimizes the average distance to other possible sentences weighted by their estimated probability).

Efficient algorithms have been devised to re score lattices represented as weighted finite state transducers with edit distances represented themselves as a finite state transducer verifying certain assumptions.[57]

For instance, similarities in walking patterns would be detected, even if in one video the person was walking slowly and if in another he or she were walking more quickly, or even if there were accelerations and deceleration during the course of one observation.

DNN architectures generate compositional models, where extra layers enable composition of features from lower layers, giving a huge learning capacity and thus the potential of modeling complex patterns of speech data.[66]

success of DNNs in large vocabulary speech recognition occurred in 2010 by industrial researchers, in collaboration with academic researchers, where large output layers of the DNN based on context dependent HMM states constructed by decision trees were adopted.[67][68] [69]

See also the related background of automatic speech recognition and the impact of various machine learning paradigms including notably deep learning in recent

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.[75]

Consequently, CTC models can directly learn to map speech acoustics to English characters, but the models make many common spelling mistakes and must rely on a separate language model to clean up the transcripts.

the first end-to-end sentence-level lip reading model, using spatiotemporal convolutions coupled with an RNN-CTC architecture, surpassing human-level performance in a restricted grammar dataset.[79]

Unlike CTC-based models, attention-based models do not have conditional-independence assumptions and can learn all the components of a speech recognizer including the pronunciation, acoustic and language model directly.

Typically a manual control input, for example by means of a finger control on the steering-wheel, enables the speech recognition system and this is signalled to the driver by an audio prompt.

Simple voice commands may be used to initiate phone calls, select radio stations or play music from a compatible smartphone, MP3 player or music-loaded flash drive.

car models offer natural-language speech recognition in place of a fixed set of commands, allowing the driver to use full sentences and common phrases.

Front-end speech recognition is where the provider dictates into a speech-recognition engine, the recognized words are displayed as they are spoken, and the dictator is responsible for editing and signing off on the document.

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.

One of the major issues relating to the use of speech recognition in healthcare is that the American Recovery and Reinvestment Act of 2009 (ARRA) provides for substantial financial benefits to physicians who utilize an EMR according to 'Meaningful Use' standards.

The use of speech recognition is more naturally suited to the generation of narrative text, as part of a radiology/pathology interpretation, progress note or discharge summary: the ergonomic gains of using speech recognition to enter structured discrete data (e.g., numeric values or codes from a list or a controlled vocabulary) are relatively minimal for people who are sighted and who can operate a keyboard and mouse.

A large part of the clinician's interaction with the EHR involves navigation through the user interface using menus, and tab/button clicks, and is heavily dependent on keyboard and mouse: voice-based navigation provides only modest ergonomic benefits.

By contrast, many highly customized systems for radiology or pathology dictation implement voice 'macros', where the use of certain phrases – e.g., 'normal report', will automatically fill in a large number of default values and/or generate boilerplate, which will vary with the type of the exam – e.g., a chest X-ray vs.

The results are encouraging, and the paper also opens data, together with the related performance benchmarks and some processing software, to the research and development community for studying clinical documentation and language-processing.

In these programs, speech recognizers have been operated successfully in fighter aircraft, with applications including: setting radio frequencies, commanding an autopilot system, setting steer-point coordinates and weapons release parameters, and controlling flight display.

The acoustic noise problem is actually more severe in the helicopter environment, not only because of the high noise levels but also because the helicopter pilot, in general, does not wear a facemask, which would reduce acoustic noise in the microphone.

Many ATC training systems currently require a person to act as a 'pseudo-pilot', engaging in a voice dialog with the trainee controller, which simulates the dialog that the controller would have to conduct with pilots in a real ATC situation. Speech

In theory, Air controller tasks are also characterized by highly structured speech as the primary output of the controller, hence reducing the difficulty of the speech recognition task should be possible.

Students who are blind (see Blindness and education) or have very low vision can benefit from using the technology to convey words and then hear the computer recite them, as well as use a computer by commanding with their voice, instead of having to look at the screen and keyboard.[91]

Students who are physically disabled or suffer from Repetitive strain injury/other injuries to the upper extremities can be relieved from having to worry about handwriting, typing, or working with scribe on school assignments by using speech-to-text programs.

Use of voice recognition software, in conjunction with a digital audio recorder and a personal computer running word-processing software has proven to be positive for restoring damaged short-term-memory capacity, in stroke and craniotomy individuals.

For individuals that are Deaf or Hard of Hearing, speech recognition software is used to automatically generate a closed-captioning of conversations such as discussions in conference rooms, classroom lectures, and/or religious services.[93]

Speech recognition is also very useful for people who have difficulty using their hands, ranging from mild repetitive stress injuries to involve disabilities that preclude using conventional computer input devices.

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.[96]

Also the whole idea of speak to text can be hard for intellectually disabled person's due to the fact that it is rare that anyone tries to learn the technology to teach the person with the disability.[97]

the 10 digits 'zero' to 'nine' can be recognized essentially perfectly, but vocabulary sizes of 200, 5000 or 100000 may have error rates of 3%, 7% or 45% respectively.

When a person reads it's usually in a context that has been previously prepared, but when a person uses spontaneous speech, it is difficult to recognize the speech because of the disfluencies (like 'uh' and 'um', false starts, incomplete sentences, stuttering, coughing, and laughter) and limited vocabulary.

As the more complex sound signal is broken into the smaller sub-sounds, different levels are created, where at the top level we have complex sounds, which are made of simpler sounds on lower level, and going to lower levels even more, we create more basic and shorter and simpler sounds.

Once these sounds are put together into more complex sound on upper level, a new set of more deterministic rules should predict what new complex sound should represent.

Each frame has a unit block of sound, which are broken into basic sound waves and represented by numbers which, after Fourier Transform, can be statistically evaluated to set to which class of sounds it belongs.

The nodes in the figure on a slide represent a feature of a sound in which a feature of a wave from the first layer of nodes to the second layer of nodes based on statistical analysis.

For example, activation words like 'Alexa' spoken in an audio or video broadcast can cause devices in homes and offices to start listening for input inappropriately, or possibly take an unwanted action.[103]

The other adds small, inaudible distortions to other speech or music that are specially crafted to confuse the specific speech recognition system into recognizing music as speech, or to make what sounds like one command to a human sound like a different command to the system.[105]

A good insight into the techniques used in the best modern systems can be gained by paying attention to government sponsored evaluations 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).

Deng published near the end of 2014, with highly mathematically-oriented technical detail on how deep learning methods are derived and implemented in modern speech recognition systems based on DNNs and related deep learning methods.[70]

Yu provides a less technical but more methodology-focused overview of DNN-based speech recognition during 2009–2014, placed within the more general context of deep learning applications including not only speech recognition but also image recognition, natural language processing, information retrieval, multimodal processing, and multitask learning.[66]

Cambridge Sound Management

Architects consider a variety of elements to address noise control and speech privacy that either Absorb, Block, or Cover sound, and are collectively called the ABC’s of acoustic design.

Oftentimes, absorbing materials (carpet, ceiling tiles, etc.) and blocking structures (walls, cubicle partitions, etc.) are costly and underused, particularly in modern offices.

Sound masking makes a building seem quieter by raising the ambient noise level of an environment and making speech noise less intelligible and therefore less distracting.

Sound masking is an ambient sound, similar to the sound of airflow, that’s specifically engineered to the frequency of human speech to target conversational distractions and make them less distracting.

Machine Learning is Fun Part 6: How to do Speech Recognition with Deep Learning

First, we’ll replace any repeated characters a single character: Then we’ll remove any blanks: That leaves us with three possible transcriptions — “Hello”, “Hullo” and “Aullo”.

The trick is to combine these pronunciation-based predictions with likelihood scores based on large database of written text (books, news articles, etc).

Of our possible transcriptions “Hello”, “Hullo” and “Aullo”, obviously “Hello” will appear more frequently in a database of text (not to mention in our original audio-based training data) and thus is probably correct.

It will always understand it as “Hello.” Not recognizing “Hullo” is a reasonable behavior, but sometimes you’ll find annoying cases where your phone just refuses to understand something valid you are saying.

You get a bunch of data, feed it into a machine learning algorithm, and then magically you have a world-class AI system running on your gaming laptop’s video card… Right?

To build a voice recognition system that performs on the level of Siri, Google Now!, or Alexa, you will need a lot of training data — far more data than you can likely get without hiring hundreds of people to record it for you.

If you have an Android phone with Google Now!, click here to listen to actual recordings of yourself saying every dumb thing you’ve ever said into it: So if you are looking for a start-up idea, I wouldn’t recommend trying to build your own speech recognition system to compete with Google.

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