Speech Recognition: A Review of Literature. Abstract Speech recognition is a process of identifying what a person speaks into a mike or any other similar hardware and reflects its meaning in any required form such as text, image or any event. The developed system works for Punjabi as well as English digits recognition. Signal Processing and Speech Communication Laboratory.
Streaming Models for Joint Speech Recognition and Translation
Speech Recognition using Neural Networks – IJERT
AbstractSpeech is the most common way for humans to interact. Since it is the most effective method for communication, it can be also extended further to interact with the system. As a result, it has become extremely popular in no time. The speech recognition allows system to interact and process the data provided verbally by the user. Ever since the user can interact with the help of voice the user is not confined to the alphanumeric keys. Speech recognition can be defined as a process of recognizing the human voice to generate commands or word strings. Speech recognition activity can be performed after having a knowledge of diverse fields like linguistic and computer science.
35 research papers and projects in Speech Recognition – Download
Being that its creation was only a few decades ago and that it has come this far, there is still so much room for advancement in years to come. Today we use this software for many things such as call centers when we call companies and corporations that receive a lot of incoming business or with the blind or authors when they speak into a microphone and what they speak is made into a document. As we take a deeper look into the creation of automatic speech recognition we will address its possible disadvantages and advantages that will give us a better understanding of this new technology. As stated before, automatic technology was only launched decades ago, to be specific, in the s. Notice that only a single voice could be used once when first tested on the recognition software because technology was not advanced enough to understand more than one pitch or even different sexes.
Our novel Transformer-based acoustic model achieved the lowest word error rate WER on LibriSpeech, one of the most popular public datasets in speech recognition — making hybrid performance on par with alternative end-to-end approaches. This is an important milestone because hybrid systems are a relatively mature technology and still serving billions of people every day. This is the first time the popular Transformer architecture has been successfully applied to acoustic modeling for for hybrid ASR.