
Automated speech recognition (ASR) has made unimaginable advances prior to now few years, particularly for broadly spoken languages reminiscent of English. Previous to 2020, it was sometimes assumed that human skills for speech recognition far exceeded automated techniques, but some present techniques have began to match human efficiency.
The purpose in growing ASR techniques has all the time been to decrease the error rate, no matter how individuals carry out in the identical setting. In spite of everything, not even individuals will acknowledge speech with 100% accuracy in a loud setting.
In a brand new research, UZH computational linguistics specialist Eleanor Chodroff and a fellow researcher from Cambridge University, Chloe Patman, in contrast two fashionable ASR techniques—Meta’s wav2vec 2.0 and Open AI’s Whisper—towards native British English listeners. They examined how nicely the techniques acknowledged speech in speech-shaped noise (a static noise) or pub noise, and produced it with or with no cotton face masks.
The research is published within the journal JASA Specific Letters.
Newest OpenAI system higher—with one exception
The researchers discovered that people nonetheless maintained the sting towards each ASR techniques. Nevertheless, OpenAI’s most up-to-date giant ASR system, Whisper large-v3, considerably outperformed human listeners in all examined circumstances besides naturalistic pub noise, the place it was merely on par with people. Whisper large-v3 has thus demonstrated its skill to course of the acoustic properties of speech and efficiently map it to the meant message (i.e., the sentence).
“This was impressive as the tested sentences were presented out of context, and it was difficult to predict any one word from the preceding words,” Chodroff says.
Huge coaching knowledge
A better have a look at the ASR techniques and the way they have been skilled exhibits that people are nonetheless doing one thing outstanding. Each examined techniques contain deep learning, however essentially the most aggressive system, Whisper, requires an unimaginable quantity of coaching knowledge.
Meta’s wav2vec 2.0 was skilled on 960 hours (or 40 days) of English audio knowledge, whereas the default Whisper system was skilled on over 75 years of speech knowledge. The system that really outperformed human skill was skilled on over 500 years of nonstop speech.
“Humans are capable of matching this performance in just a handful of years,” says Chodroff. “Appreciable challenges additionally stay for automatic speech recognition in nearly all different languages.”
Various kinds of errors
The paper additionally reveals that people and ASR techniques make several types of errors. English listeners nearly all the time produced grammatical sentences, however had been extra prone to write sentence fragments, versus making an attempt to offer a written phrase for every a part of the spoken sentence.
In distinction, wav2vec 2.0 ceaselessly produced gibberish in essentially the most troublesome circumstances. Whisper additionally tended to provide full grammatical sentences, however was extra prone to “fill in the gaps” with utterly unsuitable info.
Extra info:
Chloe Patman et al, Speech recognition in adversarial circumstances by people and machines, JASA Specific Letters (2024). DOI: 10.1121/10.0032473
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Bar chatter: Automated speech recognition rivals people in noisy environments (2025, January 14)
retrieved 14 January 2025
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