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This article was published as a part of the Data Science Blogathon.Introduction
Most recent speech recognition models often rely on large supervised datasets, which are unavailable for many low-resource languages; this poses a challenge in creating a speech recognition model inclusive of all languages. To address this, researchers from Carnegie Mellon University have proposed a method to create speech recognition systems that don’t require any audio dataset or pronunciation lexicon for the target language. The only prerequisite is having access to raw text datasets or a set of n-gram statistics for the target language.
In this article, we will take a look at this proposed method in further detail. Let’s get started!Highlights
ASR2K is a speech recognition pipeline that does not require audio for the target language. The only assumption is that access to raw text datasets or a set of n-gram statistics is available.
The speech pipeline in ASR2K comprises three components i.e., acoustic, pronunciation, and language models.
The acoustic and pronunciation models employ multilingual models without supervision, in contrast to the conventional pipeline. The language model is created using the raw text dataset or n-gram statistics.
This approach was used for 1909 languages using Crúbadán, a large endangered languages n-gram database. Then it was subsequently tested on 129 languages (34 languages from the Common Voice dataset + 95 languages from the CMU Wilderness dataset).
On testing, only 50% CER (Character Error Ratio) and 74% WER (Word Error Ratio) were achieved on the Wilderness dataset using Crúbadán statistics. These results were subsequently improved to 45% CER and 69% WER when using 10,000 raw text utterances.Existing Methods
Modern architectures typically require thousands of hours of training data for the target language to perform well. However, there are about 8000 languages spoken worldwide, the majority of which lack audio or text datasets.
Some attempts have decreased the training set’s size by leveraging pre-trained features from self-supervised learning models. However, these models continue to rely on a small amount paired supervised data for word recognition.Proposed Methodology for ASR2K
The speech pipeline comprises the acoustic, pronunciation, and language models.
The acoustic model should still recognize the target languages’ phonemes even if the languages are unseen in the training set.
The pronunciation model is a G2P (grapheme-to-p oneme) model that can predict phoneme pronunciation given a sequence of graphemes.
Both acoustic and pronunciation models can be trained using supervised datasets from high-resource languages and then applied to the target language with the help of some linguistic knowledge. The acoustic and pronunciation models employ multilingual models and can be used in a zero-shot learning setting without supervision, in contrast to the standard pipeline.
A lexical graph is created by encoding the approximate pronunciation of each word using the pronunciation model. Finally, the raw texts or n-gram statistics are used to create a language model combined with the pronunciation model to create a (weighted finite-state transducer) WFST decoder.
The joint probability over speech audio (X) and speech text (Y) is defined as follows:
, where P is the phoneme sequence corresponding to the text (Y). Typically, the pronunciation model ppm is modeled as a deterministic function δpm. Moreover, only the language model can be estimated from the text, whereas the acoustic model and pronunciation model are approximated using zero-shot learning or transfer learning from other high-resource languages; hence pˆam, ˆδpm corresponds to the approximated acoustic model and pronunciation model. The prior factorization can be roughly represented as:
, where Pˆ = ˆδpm(Y ) is the approximated phonemes.
To analyze the pipeline more effectively with small test sets, an approach is used to decompose the observed errors into acoustic/pronunciation model errors and language model errors.Evaluation Results
This approach was used for 1909 languages using Crúbadán, a large endangered languages n-gram database. Then it was tested on 129 languages (34 languages from the Common Voice dataset + 95 languages from the CMU Wilderness dataset).
1) Evaluating the Acoustic model using PER: The acoustic model is evaluated using the phoneme error rate (PER) metric. It offers many useful insights about acoustic models.
Table 1 illustrates the performance across 4 acoustic models. The baseline acoustic model has approximately 50% PER, and half of the errors are deletion errors. It turns out that domain and language mismatches are the primary causes of the deletion errors. The SSL-based models were used to boost the robustness, which lowered the error rate by 5%. Remarkable improvement is obtained from the deletion reduction. In addition, it was found that the XLSR model performed the best, so it was used as the primary model in the pipeline.
Table 1: Average results (PER) of the acoustic model in all test languages.
without any text dataset: it achieved 65% and 50% CER on two datasets. Next, the training sets 1k, 5k, and 10k text utterances are used to train the model without Crúbadán. This improves the performance remarkably, considering the training text datasets are in the same domain as the test dataset. A 10k text dataset achieved 51% and 45% CER.
Furthermore, the effect of using Crúbadán and text language models in conjunction was also evaluated. However, this approach didn’t improve the performance since there is a domain mismatch between the two models.
Table 2: Average Performance of the language model on all testing languages in terms of CER and WER under different resource conditions.
These results are a milestone considering it is the first attempt to build an audio-free speech recognition pipeline for approximately 2000 languages.Conclusion
In this article, we explored an audio-free speech recognition pipeline (ASR2K) that doesn’t require any audio dataset or pronunciation lexicon for the target language. The key takeaways from this post are as follows:
ASR2K is a speech recognition pipeline for 1909 languages that don’t require audio for the target language. The only prerequisite is having access to raw text datasets or a set of n-gram statistics.
The speech pipeline comprises the acoustic, pronunciation, and language models.
Only the ASR2K language model can be estimated from the text in the proposed pipeline. In contrast, the acoustic and pronunciation models are approximated using transfer learning from other high-resource languages or zero-shot learning.
The raw texts or n-gram statistics are used to build a language model combined with the pronunciation model to build a WFST decoder.
On testing this approach on 129 languages across two datasets, ie. Common Voice and CMU Wilderness dataset, 50% CER and 74% WER were achieved on the Wilderness dataset using Crúbadán statistics only. These results were subsequently improved to 45% CER and 69% WER when using 10,000 raw text utterances.
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Vamsi is a tech and WordPress geek who enjoys writing how-to guides and messing with his computer and software in general. When not writing for MTE, he writes for he shares tips, tricks, and lifehacks on his own blog Stugon.
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By Vesko Garčević
“You are the leader of your nation. I wish you to be the leader of the world. Being the leader of the world means to be the leader of peace!” These were the words with which Ukrainian President Volodymyr Zelensky concluded his virtual address to the US Congress on Wednesday. Much of his speech was delivered in Ukrainian, but the final points were made in English.
With a powerful, and at moments emotional, statement, the Ukrainian president again confirmed that he had a firm grasp on how to present his nation’s case to a foreign audience. Speaking to the British Parliament several days ago, he referenced Winston Churchill, Great Britain’s prime minister during World War II. In his address to the Americans, Zelensky compared Russian air raids and shelling of Ukrainian cities to Pearl Harbor and the September 11 terrorist attacks.
He referred to the monument to US presidents at Mount Rushmore and the “I Have a Dream” speech by Martin Luther King, Jr. (GRS’55, Hon.’59), saying that his dream is to have “a free and peaceful sky above Ukraine,” stating that Ukrainians are not fighting only for their freedom, but also “for the values of Europe and the world.” His plea for the United States and NATO to “close the sky” to Russian strikes was illustrated by graphic images of ruined Ukrainian cities, the suffering of ordinary citizens, and dead and injured children.
Indeed, Zelensky made it difficult for the United States and its allies to say no to his appeals. However, once the curtain has fallen, the question remains whether the United States can do more to help Ukraine, and if the answer is yes, what can it do?
Despite Texas Republican Congressman Michael McCaul’s appealing statement that “a lot of the members were in tears watching Zelensky’s address,” a number of factors determine the scope of the US involvement in the crisis. Most important, Ukraine is a NATO partner, but not a NATO member. The principle of collective defense as stipulated in Article 5 of the Washington [NATO] treaty doesn’t apply in this case, and therefore the Alliance is not obliged to directly intervene. It is hard to expect the United States or NATO to send troops into Ukraine.
In fact, Zelensky’s frequent calls for a no-fly zone over Ukraine has forced Washington to confront the so-called “escalation paradox.” Since the beginning of the invasion, Western assistance to Ukraine has resembled something akin to walking a tightrope. Washington and the European allies can’t stand still and look at the aggression without taking measures against Russia, but they have to do it carefully to avoid further escalation of the situation. A no-fly zone, for example, would lead to direct involvement in the war and confrontation with the Russians. They [no-fly zones] are designed to deny the use of airspace; it implies the engagement with aircraft that refuse to comply with them, in this case with Russian combat fighter jets.
Furthermore, President Biden is perceived as somebody who epitomizes a “noninterventionist instinct.” He opposed President Obama’s intervention in Libya as well as his surge of troops in Afghanistan. He resolutely defended his order to withdraw US forces from Afghanistan last year. And finally, he is very well aware of possible risks that direct military involvement in Ukraine would bear.
Against this backdrop, a mix of the rigorous sanctions and military aid seems to be the most realistic way ahead for Washington in the weeks to come. When it comes to military and humanitarian assistance, the United States has made several steps since the outbreak of the conflict: it released about $350 million in arms at the beginning of the conflict. Weapons shipments to Ukraine over the past year—three in total—come in at more than $1 billion. And President Biden is expected to announce another $800 million toward a fresh round of military aid in the coming days, including anti-tank missiles and anti-aircraft systems. The administration is planning to ramp up the sanctions against top Russian officials and those in President Vladimir Putin’s inner circle, according to multiple US officials. Last week, Congress approved a $13.6 billion package of humanitarian, security, and economic assistance, while the US Agency for International Development (USAID) has provided more than $100 million in humanitarian aid and critical relief supplies.
Since Moscow has thus far failed to stage a blitzkrieg attack in Ukraine, the longer war lasts, the more obvious the fissures in the Russian approach to training, equipping, and organizing become. The trajectory of the war, coupled with the growing civilian and military casualties, huge civilian suffering, and millions of refugees, tells us again that diplomacy remains the only option in Ukraine. The Russians’ difficulty in quickly fulfilling their objectives (if they’d ever set clear objectives), and the growing feeling that the operation is costly to maintain, may create a favorable environment for negotiation. It is unrealistic to suppose a comprehensive diplomatic solution will meet the interests of Kyiv and Moscow in the coming days or even weeks. The right to self-defense is guaranteed to Ukrainians by the UN Charter, and they have the right to do their utmost to protect the country. Therefore, a more realistic approach in this moment would be to build a peace step-by-step by reducing risks of further escalation. A ceasefire in Ukraine would be a milestone in a (long?) negotiating process that may guarantee a lasting peaceful solution for the country.
Vesko Garčević is a BU Frederick S. Pardee School of Global Studies professor of the practice of international relations and a former ambassador from Montenegro to Brussels (NATO) and Vienna (Organization for Security and Cooperation in Europe [OSCE]) and other international organizations. He can be reached at [email protected].
Critics of facial recognition took a big hit today, as London’s Metropolitan Police service has announced they will begin using Live Facial Recognition (LFR) technology on citizens in the massive European city.
As perhaps one of the most controversial technologies in recent memory, debate about the use of facial recognition to police citizens has been tumultuous to say the least. From citywide condemnations to proposed EU bans, lawmakers are torn between the functionality of new tech and a commitment to privacy.
Now, with the Met announcing its latest decision to fully utilize LFR throughout the city, it’s safe to say the mainstream age of facial recognition technology is upon use.Met Police Announcement
In a statement released today, London’s Metropolitan Police announced it will “begin the operational use of Live Facial Recognition technology,” specifically to help “tackle serious crime, including serious violence, gun and knife crime, and child sexual exploitation.” And of course, they have to mention how safe they were going to keep everyone and how much everyone supports this decision.
“This is an important development for the Met and one which is vital in assisting us in bearing down on violence,” said Nick Ephgrave, Assistant Commissioner at the Met, in the statement. “As a modern police force, I believe that we have a duty to use new technologies to keep people safe in London. Independent research has shown that the public support us in this regard.”
The Met claims that the LFR system — which will be fully operational, rather than on a trial basis — is 70% effective at identifying subjects, and hardly ever falsely identifies anyone. Honestly, those numbers don’t sound great already, and they sound even worse when you consider the person who conducted the only independent review of the system says it was a lot closer to 19%.
“I stand by our findings,” said Peter Fussey, a surveillance expert at Oxford University, the Guardian. “I don’t know how they get to 70%.”
While it’s certainly one of the larger cities to implement this kind of technology into their police system, London is not the first. South Wales has been using it for less than a year, after a number of court cases opened the doors for its use. Unfortunately, that didn’t stop people from responding to the news with a lot of reasonable negativity.The Swift and Brutal Response
As you’d expect, the response to the Met’s decision was as passionate as it was pointed. Critics called the use of facial recognition by the London police service everything from “a huge threat to human rights,” to “a dangerous, oppressive and completely unjustified move.” And they certainly didn’t stop at name calling.
“This technology puts many human rights at risk, including the rights to privacy, non-discrimination, freedom of expression, association and peaceful assembly,” said Allan Hogarth, from Amnesty International UK. “This is no time to experiment with this powerful technology that is being used without adequate transparency, oversight and accountability.”
Despite the notable backlash, the Met is moving forward with this decision. And while the citywide support they claim to have might be suspect, having the mayor on your side certainly doesn’t hurt your cause. That is, as long as they utilize the technology correctly and morally.
“New technology has a role in keeping Londoners safe, but it’s equally important that the Met are proportionate in the way it is deployed and are transparent about where and when it is used in order to retain the trust of all Londoners,” said Sadiq Khan, the Mayor of London. “City Hall and the Ethics Panel will continue to monitor the use of facial recognition technology as part of their role in holding the Met to account.”
Criticisms of the accuracy and morality of using facial recognition technology to monitor citizens are more than justified. The reality is though, if you’ve got the mayor on your side, all the backlash in the world isn’t going to stop you, even if that’s exactly what’s happening.Global Facial Recognition Backlash
London might be on board with facial recognition technology, but they don’t have a lot of company. While the city has been a bit more prone to surveillance the others in recent years, the global backlash to the development of the controversial tech has been substantial, and it’s taken a lot of forms.
San Francisco has issued a citywide ban on the technology. Amazon shareholders stopped the sale of the company’s Rekognition technology to law enforcement agencies. Even the EU is considering a temporary ban on all facial recognition software to catch up to the fast evolving tech, which after this announcement, would make some serious waves amongst the Brexit decision.
Even if support for facial recognition technology use in law enforcement is high — which, in the US, it surprisingly is — it’s hard to argue that it’s accurate enough to be utilizing it outside of a trial basis. Studies have shown the tech to be racist, sexist, and generally wrong to an embarrassing level for anything being used to decide who goes to jail and who doesn’t. And if you disagree, just wait until you’re one of the people being falsely identified.
Problem recognition is the initial stage in the lengthy consumer decision-making process and is critical for several reasons. For starters, it indicates why customers desire to acquire what they wish to buy. Second, it provides a clear direction for his upcoming purchasing behavior phases, such as information search and alternative evaluation: Third, it gives marketers enormous leeway in influencing how purchasers identify or do not realize their demands. As a result, a “virtuous loop” arises between consumer identification of an issue and marketers’ stimuli or signals that drive consumers to react in a desired manner.
It is also possible to categorize customers based on their distinct problem-recognition techniques. The first type of consumer believes they have a problem when their product fails to work adequately. For example, a consumer-purchased wristwatch no longer tracks precise time. The second sort of client recognizes a need or problem not because an existing product has failed to meet their expectations but because they want to own something new. This helps to explain why Titan watches found a ready market, although HMT wristwatches were essentially high-performance items.Problem Recognition
Recognition of a need or problem may involve straightforward or complicated setups, depending on the circumstances. The phrase “simple problem recognition” refers to standard requirements that may be met virtually effortlessly. For instance, you could have bought a cool drink after seeing a soft drink kiosk while shopping with your buddies. This could also lessen the monotony of waiting for your buddies to show up there.
Complex issue recognition refers to the condition in which problem recognition emerges gradually but unambiguously over an extended period. The ideal state of the consumer’s thinking and the actual state diverge noticeably at some point. He becomes aware of the necessity as a result. For instance, a car owner could start to think about exchanging their vehicle for a new one after several years of use. Any factors, from rising repair costs to the availability of multiple new models, might be to blame. In the case of a straightforward problem, a customer will undoubtedly remember precisely the circumstances surrounding his need for the goods.
Nonetheless, even a highly cautious customer may err in complicated forms. Different levels of participation lead to these differences in remembering or forgetting to remember the awareness of the need. You will likely remember precisely what occurred before you contemplated purchasing that good or service, and vice versa, the more complex the purchase situation. In addition, many buying demands are identified and satisfied when shopping. They are referred to as impulsive purchases. The “perceived gap or mismatch between the existing and intended consumer positions for a certain product and service” is what is known as an issue.
The current consumer stance shows the customer’s thoughts and feelings on consuming or non-consumption a particular product. The intended customer scenario refers to his hopes and plans for using a particular good or service, whether they are used or not. The customer’s requirements are frequently shaped by the apparent chasm or difference between these two phases. So, there will be a perceived discrepancy between customers’ existing and ideal circumstances as they mature physically, financially, and psychologically.
A growing youngster will want a tricycle initially, followed by a bicycle and, maybe, a moped or motorcycle. Similarly, a homemaker will make shopping plans if she discovers that her kitchen or other domestic supplies are running low. However, natural variables like stock shortages, organic development, and financial growth take time to distinguish between mental states. Because of this, various marketing stimuli or activities often widen or accelerate the gaps between the existing and intended states of mind. These marketing initiatives either affect the intended state of mind, the present state of mind, or both.
Marketers may “induce” consumers’ unhappiness with the current selection of goods and services, given their current state of mind. This trend is an example of the introduction of perfectly angled toothbrushes, iodized salt, pure spices, etc. The marketers may even persuade the customers that the items are unnecessary. This displeasure is typically directed against “obsolete” functionality, design, and technology. However, market initiatives to address the past or present consumption situation are relatively modest. They have a rather gloomy vibe about them as well. So, marketers make most of their efforts to shape consumers’ “desired” states of mind.
The desired customer position is attained by promoting innovative applications for the items already on the market or by including newer product features. These marketing stimuli frequently assure customers of greater levels of enjoyment. Moreover, the marketer uses more excellent appeals and incentives to highlight the discrepancy between the actual and ideal consumption conditions, hastening the problem’s identification. The introduction of 3-door refrigerators, 40-channel Televisions, geared bicycles, consumer financing, and the availability of simple credit facilities are a few of the numerous techniques that Indian marketers have recently employed in this direction.Threshold level in Problem Recognition
The perceived difference between consumers’ actual and desired states of mind, which is significantly impacted by marketing stimuli, is represented by issue recognition. Not every “gap” between these states of mind for a product or service will automatically result in its “need.” Instead, the perceived gap must exceed a certain threshold to trigger the purchase process. The phrase “minimum amount of tension, energy, or intensity which is essential for the experience to occur” describes the threshold level.
As a result, marketing activities are focused on widening the gap between customers’ existing and desired mental states and raising the tension level necessary to achieve need awareness. Examples of these marketing initiatives include the manufacturers’ own simple credit or repayment options for durable and non-durable items and joint ventures with organizations like banks or lease finance companies. Marketers frequently inflame consumer anxiety by contrasting customers with others who have purchased their goods. When the stress rises, a craving for acknowledgment results.Problem Recognition in the Industrial Buying Process
When an employee of the firm identifies a problem or need that may be resolved by obtaining an item or a service, the purchasing process starts. Identification of a problem can emerge from both internal and external cues. Internally, the following are the most frequent occurrences that result in problem recognition −
The business intends to create a brand-new product and needs new machinery and supplies to manufacture it.
A piece of equipment has to be replaced or repaired.
The business explores alternatives after certain acquired materials appear subpar.
The purchase manager finds opportunities for better deals or higher-quality products
Plain stockout circumstances.Conclusion
In most purchase situations, problem or need recognition is the first stage of the programmed buying decision process. Problem recognition refers to consumer attention to the gap between the ideal or desired state and the existing state of mind. Buying motives are the chief contribution to this gap, and it usually determines the content and direction of the rest of the decision-making process.
Programming languages are a crucial part of ethical hacking tasks and operations
Ethical hacking is currently one of the most sought-after skills for tech professionals. Even though hacking refers to the unlawful use or access to someone’s resources or information,
is just the opposite. It refers to the process where professionals hack into the system with permission, without any malicious intent, and this process is referred to as ethical hacking. Integrating ethical hacking will facilitate the organization to have access to high levels of security in the existing infrastructure and provide instruction providing system accessibility and upholding its integrity at the same time. Nowadays, tech professionals use
and high-tech computing systems to ensure efficient ethical hacking. These top programming languages can provide several accessible facilities and also ensure that the system is secure and performing without any compromise. In this article, we have listed the top
for ethical hacking in 2023.Python
Python is one of the most popular and widely used programming languages in the tech industry. It is also a de-facto language for ethical hackers. Python is a dynamic
that is used by ethical hackers for scripting their on-demand hacking programs efficiently. Starting from testing the authenticity of corporate servers, detecting impending threats, to automating the hacking process, Python has emerged as a go-to language for ethical hackers.Java
C programming language is considered to be a holy grail of modern
that are extensively used for industrial purposes. The low-level nature of C provides an edge over other programming languages since it can be used for hack programming when it comes to low-level hardware components such as the RAM. The language also gives penetration testers the ability to write exponentially fast socket programming scripts. Besides, experienced security professionals also use C to simulate the library hijacking attack.Perl
Perl is a scripting language that can be used to handle a wide range of functionalities. System administrators and network programmers use Perl for various purposes, but especially to perform network routing operations. The professionals can also augment existing scripts to send copies of information to different locations to spread the information of data theft attempts and such others.PHP
Hypertext Processor or PHP is a dynamic programming language that is used widely in web and mobile applications. Most of the websites on the internet are based on CMSs like WordPress or Drupal. Integrating PHP on these websites can detect compromising networks. The skill of understanding and deploying this hacking programming language is essential for professionals wishing to develop server hacking programs.C++
C++ is undoubtedly one of the best programming languages for hacking corporate software. This language is capable of providing a low level of access necessary to analyze the machine code and neglect such bypass schemes. Industry professionals who wish to crack any industry software or even build an efficient hacking program for individual professional purposes should master the skills of C++ programming.Ruby
Ruby is a web-focused programming language and is among the top languages for hacking multi-purpose corporate systems. With the help of Ruby, professionals can easily automate programs and offer super flexibility for writing scripts for hack programming. Also, the language enables efficiency while writing functional codes and chain commands.SQL
SQL is a vital and one of the most vital and favourite programming languages for ethical hacking professionals. SQL or Structured Query Language is used for querying and fetching information from databases. Without an in-depth understanding of SQL, professionals will be unable to counteract database attacks.Bash
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