Why Isn’t Speech Recognition Software More Accurate?

The most straightforward definition of speech recognition is a computer program made to be trained to learn from the human voice and interpret it meaningfully.

The speech recognition software works by splitting the audio file, analyzing even low sounds using multiple algorithms, and ultimately producing fitted text.

Natural language processing and deep learning models are primarily used to extract useful meaning in a smart way. The voice recognition used by Siri, Google Home, and Amazon Alexa in daily life is prime examples of both concepts at work in applications.

However, the successful implementation of accurate voice recognition software is hindered by many factors, thanks to the considerable variation of pronunciation by a single individual, then broadened by multiple users. This problem generates different signals in the time-frequency domain.

The variation is due to the different stresses on the vocal cords, microphone, and environmental considerations. Deep learning models, including the hidden Markov model, show potential in solving these kinds of issues. Recently, deep neural networks have performed better, and software accuracy has improved.

Is Speech Recognition Inaccurate?

Speech recognition takes a string of words as an input, but understanding which string of works they accept is tricky.

With the rapid advancement of deep learning, organizations are showing a huge interest in ASR, said Hayley Sutherland, a senior research analyst for conversational AI and intelligent knowledge discovery at IDC. She also stated that the systems currently show 75% to 85% accuracy, but the training can improve that.

Guessing the wrong word is a common issue with ASR. One way to train automatic speech recognition using deep learning algorithms is to divide the whole sentence into single to use it as a set of features, commonly known as acoustic models.

In the testing phase, these features are used to compare each word. This method works well with small sentences but loses track in large vocabulary, putting a burden computationally. To handle such complex structures, we need to improve the amount of training data.

Another problem ASR finds is that sometimes the voice is disturbed by another passing person, and the software will not understand the right words, leading to inappropriate results.

Natural language processing widely covers this domain but is still not showing enough growth towards formulating plausible sentences.

Improving Speech Recognition Cognition

One of the most significant applications of speech recognition is controlled machinery. A user can focus on manual processes while still controlling machinery with the help of ASR.

Moreover, the use of voice-controlled weapons is the most controversial application of speech recognition programs. The reliable program can handle commands by the officer with the speech input.

Another interesting example is found in the medical field. A radiologist can focus on CT scans, X-rays and, by using ASR, can generate reports. Nevertheless, ASR is helpful in automatic hotel or airline reservations.

Some of the most advanced approaches used to achieve high accuracy are based on recurrent neural networks. The end-to-end approach performs better on acoustic features rather than traditional ones.

Traditional methods use various knowledge resources, whereas end-to-end approaches simplify both the decoding and training pipeline.

This provides a powerful research platform because of RNN-transducers and attention-based algorithms. RNN-T facilitates handling the nature of speech, and the attention models work better with non-monotonic alignment issues such as translation. It is seen that with the help of enough training data, end-to-end models show potential in achieving better accuracy.

By training automated speech recognition (ASR) apps, we can recognize differences in background noises and commands from users. Furthermore, improvements in the processing lead to the point that imprecise speech like “um” and “uh” and don’t disrupt the speech recognition program from understanding.

Good speech recognition programs also provide the option of customization to the users. For example, a user can adjust attention for certain words or product references. Moreover, they would be able to tune the speaking style, volume, and pace around several persons.

To filter out inappropriate language and words is a plus feature of ASR known as profanity filtering. Speech recognition also has a positive impact on society. It provides facilities for children with disabilities including dysgraphia and dyslexia, vision impairments, and improves limited English language.

Leading An Evolution

Speech recognition is still a growing field. People can communicate with ASR with fewer or no typing commands. Many businesses are creating revenues with the speed and convenience of spoken communication with the help of this technology.

Speech Recognition has been evolving for 60 years, and the development is improving day by day, powered by artificial intelligence.

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