Facebook Makes Advancements In Automatic Speech Recognition

Facebook AI Research (FAIR) recently trained a single acoustic model for multiple languages with the aim of improving automatic speech recognition (ASR) performance on low-resource languages. The motive behind this research is to simplify the overall deployment of ASR systems that support diverse languages.

Read more: https://analyticsindiamag.com/facebook-makes-advancements-in-automatic-speech-recognition/

#deep-learning #machine-learning #artificial-intelligence

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How much does it cost to make an app like Facebook?

It is an era of technology where we can make a clone of any app like a Facebook app or any social networking app.

In the world, each and everyone has a mobile phone/cell phone. Every user mostly uses one type of social media apps in their life for being updated with the outside virtual world.

Are you inspired by any social networking app like Facebook and want to develop a Facebook clone app? If you are a business person or an enterprise, looking to develop an app like a Facebook clone app?

How To Create The Best Facebook Clone Mobile App?

Before deciding on the mobile app development of Facebook, you need to consider its features, development time, and hourly pricing because these will affect the Facebook app cost.

Key Features Of Facebook App:

• Registration - Mobile number or Email
• Automatic synchronization of contact list
• Share texts, photos, and videos instantly
• Instant notifications
• Play games, and discover nearby friends
• The intuitive search option to easily find known friends by name
• Integrated Messenger to chat with friends
• Option to access the Facebook business page

How Much Does An App Like FB Cost?

When one wants to develop an app like Facebook, the first thing that comes to his mind is how much it costs to create a Facebook-like app. The development cost of Facebook clone app is estimated on the following key factors:

• App Platform (Android, iOS (iPhone), or Windows)
• App Design
• App Size
• App Developers

Let’s describe the overall mobile app development cost by setting an hourly rate of $40 as the cost (based on the average Ukrainian and Indian market cost). With these known values, the total cost to develop an app (on one platform) similar in functionality to Facebook would be around $155,000.

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How can I create a Poll on Facebook?

How do I start or create or post a Poll on Facebook? Know the ways to add options or make a poll on Facebook Page or Messenger.

make a poll on Facebook
add options to Facebook Poll

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How much would it cost to build a social media app like Facebook or Twitter?

Social media is a computer-based technology that facilitates the sharing of ideas and information and the building of virtual networks and communities. By design, social media is internet based and offers users easy electronic communication of personal information and other content, such as videos and photos. Social media often feeds into the discovery of new content such as news stories, and discovery is a search activity.

Benefits Of Social Media:

• Increased Exposure
• Attracting customers
• Increase website traffic
• Competitive Analysis

Social media can also help build links that in turn support into SEO efforts. Many people also perform searches at social media sites to find social media content. Social connections may also impact the relevancy of some search results, either within a social media network or at a mainstream search engine. Social media is one of the most cost-efficient digital marketing methods used to syndicate content and increase your business’ visibility.

AppClues Infotech is a well renowned Mobile App development Company. We create intuitive social media apps. We specialize in designing and developing mobile apps that are fast, attractive, responsive, and easy to use. We unite with ambitious businesses and individuals to transform their ideas into neat, effective, and meaningful digital solutions. We also serve industries like Travel App, Education App, Enterprise App, Health Care App, Restaurant App, Food Ordering App, etc in very well manner and at competitive prices that suits your budget.

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Our dedicated team of developers have enough experience to build your social media apps as per your requirements. When speaking about social media app development cost no one can define accurate cost structure of app development because there are so many factors affecting like time, complexity of projects, developer’s team and also what extra features and functionality you want to include in it. So, cost is in between $15 - $25 per hour.

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Indian Accent Speech Recognition

Traditional ASR (Signal Analysis, MFCC, DTW, HMM & Language Modelling) and DNNs (Custom Models & Baidu DeepSpeech Model) on Indian Accent Speech

Courtesy_: _Speech and Music Technology Lab, IIT Madras

Image Courtesy

Notwithstanding an approved Indian-English accent speech, accent-less enunciation is a myth. Irregardless of the racial stereotypes, our speech is naturally shaped by the vernacular we speak, and the Indian vernaculars are numerous! Then how does a computer decipher speech from different Indian states, which even Indians from other states, find ambiguous to understand?

**ASR (Automatic Speech Recognition) **takes any continuous audio speech and output the equivalent text . In this blog, we will explore some challenges in speech recognition with focus on the speaker-independent recognition, both in theory and practice.

The** challenges in ASR** include

• Variability of volume
• Variability of words speed
• Variability of Speaker
• Variability of** pitch**
• Word boundaries: we speak words without pause.
• **Noises **like background sound, audience talks etc.

Lets address** each of the above problems** in the sections discussed below.

The complete source code of the above studies can be found here.

Models in speech recognition can conceptually be divided into:

• Acoustic model: Turn sound signals into some kind of phonetic representation.
• Language model: houses domain knowledge of words, grammar, and sentence structure for the language.

Signal Analysis

When we speak we create sinusoidal vibrations in the air. Higher pitches vibrate faster with a higher frequency than lower pitches. A microphone transduce acoustical energy in vibrations to electrical energy.

If we say “Hello World’ then the corresponding signal would contain 2 blobs

Some of the vibrations in the signal have higher amplitude. The amplitude tells us how much acoustical energy is there in the sound

Our speech is made up of many frequencies at the same time, i.e. it is a sum of all those frequencies. To analyze the signal, we use the component frequencies as features. **Fourier transform **is used to break the signal into these components.

We can use this splitting technique to convert the sound to a Spectrogram, where **frequency **on the vertical axis is plotted against time. The intensity of shading indicates the amplitude of the signal.

Spectrogram of the hello world phrase

To create a Spectrogram,

1. **Divide the signal **into time frames.
2. Split each frame signal into frequency components with an FFT.
3. Each time frame is now represented with a** vector of amplitudes** at each frequency.

one dimensional vector for one time frame

If we line up the vectors again in their time series order, we can have a visual picture of the sound components, the Spectrogram.

Spectrogram can be lined up with the original audio signal in time

Next, we’ll look at Feature Extraction techniques which would reduce the noise and dimensionality of our data.

Unnecessary information is encoded in Spectrograph

Feature Extraction with MFCC

Mel Frequency Cepstrum Coefficient Analysis is the reduction of an audio signal to essential speech component features using both Mel frequency analysis and Cepstral analysis. The range of frequencies are reduced and binned into groups of frequencies that humans can distinguish. The signal is further separated into source and filter so that variations between speakers unrelated to articulation can be filtered away.

a) Mel Frequency Analysis

Only **those frequencies humans can hear are **important for recognizing speech. We can split the frequencies of the Spectrogram into bins relevant to our own ears and filter out sound that we can’t hear.

Frequencies above the black line will be filtered out

b) Cepstral Analysis

We also need to separate the elements of sound that are speaker-independent. We can think of a human voice production model as a combination of source and filter, where the source is unique to an individual and the filter is the articulation of words that we all use when speaking.

Cepstral analysis relies on this model for separating the two. The cepstrum can be extracted from a signal with an algorithm. Thus, we drop the component of speech unique to individual vocal chords and preserving the shape of the sound made by the vocal tract.

Cepstral analysis combined with Mel frequency analysis get you 12 or 13 MFCC features related to speech. **Delta and Delta-Delta MFCC features **can optionally be appended to the feature set, effectively doubling (or tripling) the number of features, up to 39 features, but gives better results in ASR.

Thus MFCC (Mel-frequency cepstral coefficients) Features Extraction,

• Reduced the dimensionality of our data and
• We squeeze noise out of the system

So there are 2 Acoustic Features for Speech Recognition:

• Spectrograms
• Mel-Frequency Cepstral Coefficients (MFCCs):

When you construct your pipeline, you will be able to choose to use either spectrogram or MFCC features. Next, we’ll look at sound from a language perspective, i.e. the phonetics of the words we hear.

Phonetics

Phonetics is the study of sound in human speech. Linguistic analysis is used to break down human words into their smallest sound segments.

phonemes define the distinct sounds

• Phoneme is the smallest sound segment that can be used to distinguish one word from another.
• Grapheme, in contrast, is the smallest distinct unit written in a language. Eg: English has 26 alphabets plus a space (27 graphemes).

Unfortunately, we can’t map phonemes to grapheme, as some letters map to multiple phonemes & some phonemes map to many letters. For example, the C letter sounds different in cat, chat, and circle.

Phonemes are often a useful intermediary between speech and text. If we can successfully produce an acoustic model that decodes a sound signal into phonemes the remaining task would be to map those phonemes to their matching words. This step is called Lexical Decoding, named so as it is based on a lexicon or dictionary of the data set.

If we want to train a limited vocabulary of words we might just skip the phonemes. If we have a large vocabulary, then converting to smaller units first, reduces the total number of comparisons needed.

Acoustic Models and the Trouble with Time

With feature extraction, we’ve addressed noise problems as well as variability of speakers. But we still haven’t solved the problem of matching variable lengths of the same word.

Dynamic Time Warping (DTW) calculates the similarity between two signals, even if their time lengths differ. This can be used to align the sequence data of a new word to its most similar counterpart in a dictionary of word examples.

2 signals mapped with Dynamic Time Warping

#deep-speech #speech #deep-learning #speech-recognition #machine-learning #deep learning

Facebook Is Giving Away This Speech Recognition Model For Free

Researchers at Facebook AI recently introduced and open-sourced a new framework for self-supervised learning of representations from raw audio data known as wav2vec 2.0. The company claims that this framework can enable automatic speech recognition models with just 10 minutes of transcribed speech data.

Neural network models have gained much traction over the last few years due to its applications across various sectors. The models work with the help of vast quantities of labelled training data. However, most of the time, it is challenging to gather labelled data than unlabelled data.

The current speech recognition systems require thousands of hours of transcribed speech to reach acceptable performance. There are around 7,000 languages in the world and many more dialects. It can be said that the availability of the transcribed speech for a vast majority of languages is still negative.

To mitigate such issues, researchers open-sourced the wave2vec framework. The framework has the capability to make efficient development in Automatic Speech Recognition (ASR) for the low-resource languages.).

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