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Every one of us has come across smartphones with mobile assistants such as Siri, Alexa or Google Assistant. These are dominating and in a way invading human interactions.
The neural networks built with memory capabilities have made speech recognition 99 percent accurate. Neural networks like LSTMs have taken over the field of Natural Language Processing. A person’s speech can also be understood and processed into text by storing the last word of the particular sentence which is fascinating. To understand how these state-of-the-art applications work, lets us break down the whole process of sound recognition to machine translation.
Wave Breakdown
The audio signal is separated into different segments before being fed into the network. This can be performed with the help of various techniques such as Fourier analysis or Mel Frequency, among others. The graph below is a representation of a sound wave in a three-dimensional space. A Fourier transform can be performed on a sound wave to represent and visualise them in time or frequency domain...
What are LSTM Neural Networks?
The LSTM is a network of cells where these cells take input from the previous state ht-1 and current input xt. The main function of the cells is to decide what to keep in mind and what to omit from the memory. The past state, the current memory and the present input work together to predict the next output. The LSTM networks are popular nowadays because of their accurate performance in language processing tasks...
Conclusion
With the understanding of how to process sound on a machine, one can also work on building their own sound classification systems. But when it comes to deep learning, the data is the key. Larger the data, better the accuracy.
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Source: Analytics India Magazine
