Speech Recognition Using Artificial Intelligence

Image source: https://www.techiitalks.com/top-artificial-intelligence-technologies/?amp
  • 74% of the digital services users in 2019, used their voice-based assistant to research and buy products and services, create a shopping list, and check order status (Capgemini study).
  • 32% of the global online population in 2020 was using voice search on mobile phones.
  • 72% of people in the US use voice search through a digital assistant.
  • Nearly 50% of all web search queries were voice activated searches in 2020.
  • 2 in 5 people say voice activated devices are essential to their life.
  • 55% teens, 44% adults use voice search daily.
  • In 2019, around 3.25 billion digital assistants were in use on various devices globally.
  • Nearly 2,600 voice apps exist for consumers to download and use.
  • A much larger dataset can be processed
  • Higher accuracy compared to traditional models.
  • These models are self-learning, i.e., they can keep improving by adapting to changes in language
Image source: https://heartbeat.fritz.ai/the-3-deep-learning-frameworks-for-end-to-end-speech-recognition-that-power-your-devices-37b891ddc380
  • Language models: Produces a sequence of words
  • Pronunciation models: Tells how a particular word is spoken. Its written out as a sequence of tokens (phonemes: basic units of sound)
  • Acoustic models: These are defined from how the input token sounds and are characteristics of the audio waveform of the input.
Image source: https://blogs.fireflies.ai/how-does-voice-recognition-work/
  • Connectionist Temporal Classification (CTC)
  • Sequence-to-sequence
  • Neural Transducer (online Sequence-To-Sequence) that uses CNN(convolutional neural network)
Image source: https://zhuanlan.zhihu.com/p/28274740




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Divya Sikka

Divya Sikka


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