Voice Recognition Using Recurrent Neural Networks

If you have a computer with a microphone, you can use voice recognition software to transcribe your speech. Some computers have built-in microphones, but most specialist voice recognition software includes a microphone headset that can connect to your computer’s soundcard socket or USB connection. However, you can also use a hand-held digital recorder to dictate recordings. Some voice recognition applications can also transcribe recordings from a variety of formats.

Speech recognition

Voice recognition based on natural language processing is a form of speech technology that makes use of computer science to mimic human interaction. Instead of reading out words or phrases, it interprets the context of speech and generates text that resembles the concept. As long as the software uses the right programming techniques and has the right training, speech recognition is a viable option for many applications. The key to a successful speech recognition system is that it is intuitive and easy to use.

While speech recognition sounds simple at first, it can become a complex process once its accuracy and complexity becomes high enough. Implementing speech recognition technology before it achieves high enough accuracy can be risky and complex, especially if it is geared toward multiple markets. The benefits of speech recognition technology are numerous and include reducing workload, enhancing communication and assisting people with tasks. But some people may be skeptical of this new technology.

Distinctive speech recognition

A system that recognizes the speaker’s voice is known as a “voice-recognition” system. This technology uses the voice as input to translate a speech signal into text, and uses artificial intelligence to process the speech signals’ features. Typically, a system will recognize the words and sentences that it hears most frequently, based on the features it hears. This makes it an excellent tool for authentication.

Some systems require individual speakers to train the system. This process is known as “enrollment.” Individual speakers will read a text or isolated vocabulary into the system, and the system will then analyze the voice pattern to fine-tune recognition based on the speaker’s characteristics. There are two basic types of speech recognition systems: speaker-independent systems and speaker-dependent systems. Each one has advantages and disadvantages.

Recurrent neural network

A Recurrent Neural Network (RNN) is an artificial neural network that is trained to recognize speech using the audio signal. The RNN features bidirectional dependencies and extracts the corresponding regions for the input and output. The proposed architecture uses attention and convolutions to capture the dependencies at specific points. More details on the implementation of the RNN are available in this repository. The LAS model has been evaluated on a dataset of 3 million Google voice search utterances and 2000 hours of speech. Its recognition rate was 12.0% in both noisy and clean environments.

The recurrent neural network model uses the same principles as the feedforward neural network, but is specifically designed for sequence data. A recurrent network is trained by unfolding the network’s layers over time, gaining more temporal depth with each successive time step. A non-recurrent hidden layer is placed between the unfolded layers. The combined model can then improve speech recognition. The A, B, and C parameters are all used to enhance the model’s performance.


In the last few years, researchers have developed numerous methods to improve the accuracy of voice recognition systems. However, despite their success, these methods are far from perfect. Here, we will discuss the most promising methods for voice recognition. While RNNs are the most popular choice for speech-to-text conversion, other methods such as CNN and LSTM are also promising. This article will introduce two such methods that have already proven to be successful.

Using an attention encoder-decoder structure (ANN), RNN can learn to distinguish between different kinds of sounds. It is a time-series representation of sound that captures the long-term dependencies between the input and output sequences. Its simple form can automatically generate the next output based on the context of previous ones. Therefore, it has many potential applications in voice recognition. This article will discuss the benefits and drawbacks of using attention-based RNNs for voice recognition.


RNNN has been used to recognize voice and speech by identifying the regions that are most meaningful from an audio sequence. This model was designed to recognize speech by capturing long-term dependencies between input time-steps, and uses this information to compute output sequences based on the hidden sequences. It can be implemented in a simple form that generates the next output based on the previous context. During evaluation, the model achieved recognition rates of 10.3% and 12.0% on noisy and clean environments.

The algorithm was trained on 35 million English utterances. The dataset was 27500 hours long, and consisted of both dictation and voice search traffic. The training utterances were artificially corrupted using a room simulator. The results were then assessed on a dataset of fifteen thousand voice search and command samples. The feature extraction step produces 80-dimensional mel-scale features and computes them every 25 msec. The results are reported in the form of inference speed, or WER, based on the duration of audio.

Natural language processing

The field of natural language processing (NLP) has its roots in the 1950s. Alan Turing published an article on the topic in the journal Science. The Turing test involved an automated generation and interpretation of natural language. During this time, NLP evolved from being a linguist-based discipline to one that draws on a wider range of scientific disciplines. Today, NLP is used for many purposes, including voice recognition.

The process of analyzing speech is a complex process, which is complicated by the many different types of words and phrases in human speech. While machine learning algorithms are very effective for translating text, they have historically had difficulty interpreting human speech. Recent developments in deep learning have made this task more flexible and effective, enabling the algorithms to learn from a large volume of data. Natural language processing tools include the Python library Natural Language Toolkit, the Gensim Python library for topic modeling and document indexing, and the Intel NLP Architect.