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Automatic Speech Recognition with Deep Speech Model

Dr. Jagreet Kaur Gill | 29 June 2023

Automatic Speech Recognition with Deep Speech Model

Introduction to Automatic Speech Recognition

It was formerly impossible to convert voice to text. Over the last 30 years, speech recognition technology has advanced dramatically. Technology is getting increasingly functional and valuable as it improves day by day. The challenge is to create a model that can take audio as input and automatically generate a transcript for it. Speech recognition will assist you in converting your speech to text, and it can be utilized in a variety of applications, saving you time. Suppose speech recognition and writing technology are integrated into a mobile app that quickly records anything and returns a text transcript. In that case, it will be extremely useful because it will eliminate the need to type long paragraphs and emails, and project draughts. Record it on the device, and it will convert the text in a matter of seconds.

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What are the Benefits of Automatic Speech Recognition?

  • Consumers become more comfortable relying on voice services. Businesses are looking for new ways to integrate speech recognition into their business lines to promote growth and lower operating costs. They can save time with automatic transcription.
  • Examine client feedback on the firm's goods and services.
  • The same real-time call transcripts, enriched by AI and ML modules, may assist agents by proposing the appropriate answer depending on the conversation's stage, tone of voice, and keywords used by the customer.
  • Companies may also utilize transcripts of their top-performing agent’s conversations to teach new employees and track their overall performance.
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What are the Solutions for Automatic Speech Recognition?

Speech is the most common mode of human communication and an essential component of comprehending behavior and cognition. In Artificial Intelligence, speech recognition is a technology that allows computer systems to interpret spoken words. This information must be stored as digital signals and interpreted by software in order for machines to consume it.

They are changing the frequency of audio to make it machine-readable. This is the phase of the data preprocessing procedure where the user needs to clean up the data so that the computer can process it. If there is a huge quantity of audio and text files for training the DeepSpeech model, then there is a need to reformat it. All of the filenames and transcripts must be organized in the required manner. Once data is ready, begin installing and configuring the training environment. Install some requirements for training the DeepSpeech model once the environment is active.
DeepSpeech is an open-source Speech-to-Text engine, which uses a typical machine learning model based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make use of it more accessible.

If all preceding processes went well, use the deep speech model to train the data. When the training is finished, the inference model is saved and tested on new data. As shown in the above figure, it will convert the audio data into text.

How to Implement Automatic Speech Recognition using the Deep Speech Model?

Steps to implement automatic speech recognition using the deep speech model.

Solution Architecture

The process steps involved in the architecture of speech recognition is as follows:

Audio Input

In most cases, speech is captured and accessible in analog format. To convert analog voice to digital utilizing sampling and quantization techniques, standard sampling techniques/devices are available. A one-dimensional vector of voice samples is typically used to represent digital speech, each of which is an integer.

Data Pre-processing

Background noise and extended periods of quiet are expected in a recorded conversation. Identification and removal of silent frames and signal processing techniques to reduce/eliminate noise are all part of speech pre-processing. Some pre-processing steps include resampling, windowing, normalization, etc., based on the requirements of our model.

Then convert the audio into a mel- spectrogram or mfcc, where the frequency bands are evenly spaced, approximating the human auditory system's response better than the linearly spread frequency bands used in the standard cepstrum. This frequency warping improves the acoustic representation.

Model Training

The process steps involved in the Model Training of speech recognition is as follows:

Acoustic Model

The acoustic model consists of a deep neural network that takes audio as input and converts it to a probability over characters in the alphabet. To convert this probability to a text, an inference decoder is required.

Model Inference

Using a beam searchalgorithm, the language model helps to turn these probabilities into words of the coherent language.

Beam search Algorithm

This algorithm selects multiple alternatives for an input sequence based on conditional probability at each time.

Text Output

The Speech Decoder decodes the acoustic signal into a text sequence.

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Deployment on Edge (Raspberry Pi)

Now that you've got your models and know-how, they'll operate together to provide output. You'll need a device to test them on. Small devices like the Raspberry Pi, Android, and others can be used. The Raspberry Pi is a credit card-sized computer that connects to a computer display or television and utilizes a conventional keyboard and mouse. It's a little gadget that can scan a computer and teach individuals of all ages to edit in languages like Scratch and Python. Because of the following reasons, the Raspberry Pi is employed here:

  • Low cost.
  • Huge processing power in a compact board.
  • Many interfaces (HDMI, onboard Wi-Fi, and Bluetooth, multiple USB, Ethernet, USB powered, etc.)
  • Supports Linux, Python (making it easy to build applications)

Before executing the inference, ensure that the Python version installed on the Raspberry Pi is more significant than 3.5 and that all dependencies are correctly installed.

What are the Challenges of Automatic Speech Recognition?

The below-mentioned challenges can be faced while implementing the process of converting speech to text:

  • Speech Recognition needs a lot of data to work well, and some of it just hasn’t been collected for certain languages and topics. Without adding these, systems will remain noticeably handicapped.
  • The problem of version conflicts in python, and profound speech packages may arise.
  • May face problems in converting audio to pass into the model for training.
  • Seq2Seq modeling/encoder-decoder approach needs more time to operationalize, so the solution here is to use the pre-trained model to save time and resources; multiple GPUs are required to train the model with a lot of data.
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Speech recognition is an AI concept that enables a machine to listen to a human speech and convert it into text. Despite its complexity, Speech Recognition has a wide range of applications. Automatic Speech Recognition (ASR) algorithms are employed in various sectors today, from assisting differently-abled persons with access to computing to creating an automatic response machine. The above process will aid in the creation of a comprehensive understanding of how speech analysis works in the AI world.