What are the major challenges in speech recognition systems?

What are the major challenges in speech recognition systems?

The current challenges of speech recognition are caused by two major factors – reach and loud environments. This calls for even more precise systems that can tackle the most ambitious ASR use-cases. Think about live interviews, speech recognition at a loud family dinner or meetings with various people.

What is the process of automatic speech recognition?

It involves human programmers going through the conversation logs of a given ASR software interface and looking at the commonly used words that it had to hear but which it does not have in its pre-programmed vocabulary. Those words are then added to the software so that it can expand its comprehension of speech.

What is speech recognition and how does it work?

Speech recognition software works by breaking down the audio of a speech recording into individual sounds, analyzing each sound, using algorithms to find the most probable word fit in that language, and transcribing those sounds into text.

Why is speech recognition difficult?

Even with good phoneme recognition, it is still hard to recognize speech. This is because the word boundaries are not defined beforehand. This causes problems while differentiating phonetically similar sentences. A classic example for such sentences are “Let’s wreck a nice beach” and “Let’s recognize speech”.

What is the future of speech recognition?

The voice recognition market was valued at USD 10.70 billion in 2020 and is expected to reach USD 27.155 billion by 2026, at a CAGR of 16.8% over the forecast period 2021 – 2026. Virtual assistants are driving this growth in retail, banking, and automotive sectors, as well as personal home use.

Is CNN good for speech recognition?

Experimental results show that CNNs reduce the error rate by 6%-10% compared with DNNs on the TIMIT phone recognition and the voice search large vocabulary speech recognition tasks.

How does the ASR technology work?

Essentially, the process works as follows: An individual or a group speaks and an ASR software detects this speech. The device then creates a wave file of the words it hears. The wave file is cleaned to delete background noise and normalize the volume.

Why is ASR hard?

But one thing is certain, ASR is a challenging task. The most problematic issues being the large search space and the strong variability. We think that the problems are especially serious, because of our low tolerance to errors in the speech recognition process.

Is speech recognition a solved problem?

Ever since Deep Learning hit the scene in speech recognition, word error rates have fallen dramatically. But despite articles you may have read, we still don’t have human-level speech recognition. Speech recognizers have many failure modes.

Who invented voice recognition?

In 1952, the first voice recognition device was created by Bell Laboratories and they called it (her) ‘Audrey’. ‘Audrey’ was ground-breaking technology as she could recognize digits spoken by a single voice; a massive step forward in the digital world.

Is RNN used for speech recognition?

RNN seems to be more natural for speech recognition than MLP because it allows variability in input length [17]. The motivation for applying recurrent neural network to this domain is to take advantage of their ability to process short-term spectral features but yet respond to long-term temporal events.

Do voice assistants use NLP?

A specific subset of AI and machine learning (ML), NLP is already widely used in many applications today. NLP is how voice assistants, such as Siri and Alexa, can understand and respond to human speech and perform tasks based on voice commands.

What are the challenges in speech recognition in artificial intelligence?

These factors are often unique to a use case or a particular business need and include:

  • Background noise.
  • Punctuation placement.
  • Capitalization.
  • Correct formatting.
  • Timing of words.
  • Domain-specific terminology.
  • Speaker identification.

What is a complex voice recognition solution?

Complex products that require full voice recognition will usually need a cloud-based solution. Cloud based voice recognition systems will perform the syntactic and semantic analysis necessary for complex voice recognition capabilities.

What is the first step to recognizing speech?

The first step to recognizing speech is to actually recognize the individual words being spoken. This is usually done in hardware. The first step is converting the incoming analog speech to digital data via an Analog-to-Digital Converter (ADC).

Is there a voice recognition software for microcontrollers?

One software work-around is from a company called Sensory which offers an embedded voice recognition engine called Truly Handsfree which features a small vocabulary. It can run on an ARM Cortex-M4 microcontroller. ARM has also released an open-source library for keyword spotting applications that runs on Cortex-M microcontrollers.