Deep Speech is an open-source Speech-To-Text engine. Project Deep Speech uses TensorFlow for the easier implementation.
Deep Speech is composed of two main subsystems:
Transfer learning is the reuse of a pre-trained model on a new problem. It’s currently very popular in deep learning because it can train deep neural network with comparatively little data. This is very useful in the data science field since most real-world problems typically do not have millions of labeled data points to train such complex models.
Comparatively most native languages are lack of resources to train a neural network from scratch. This approach will be useful to create your own model using a small amount of speech to text corpus.
English and Mandarin (also some European languages) are the super example for Deep Speech ASR models. This shows that completely different linguistic features can be learned through the same network. It can be easily adapted to different languages. There are some language under progress in development.
5. Train while freezing layers in pre-trained model
*Note : Language Model is the time consuming part of this approach. Depending on the response, will make it a new article on building language model to train Deep Speech in a custom way.
#language #deep-speech #data-science #nlp #machine-learning
If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?
In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.
Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.
Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.
The popularity of React Native comes from its advantages. Some of its advantages are as follows:
Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.
React Native is very close to native. Consider the following aspects as described on the React Native website:
Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.
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Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
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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
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
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:
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,
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
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,
So there are 2 Acoustic Features for Speech Recognition:
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 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
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.
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
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
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