Adam's Apple moment for AI models

Traditional language models struggled with voice, losing time, accuracy, and nuance. Are voice-driven models the game-changing twist the world needs?

- BY PRATIMA HARIGUNANI

Dolphins, Lyrebirds, Bats, Mockingbirds, whales, and elephants may live in entirely different environments, but they have one thing in common: the power to communicate through sound. Some may be ultrasonic, some infrasonic, some mimicry, and some utterly akin to baby babble, but sound is always a crucial part of their existence—sometimes even survival.

Meta Spirit LM, GPT-4o, Gnani, DeepL and Sutra HiFi. These names seem to belong to different AI forests altogether, but they also have the denominator of sound running across them. In some way or another, many small and big players have now thrown a voice-dominant model in the Language Model (LM) ring. It is not hard to understand why when one looks at the apparent advantages. But are they also good enough to fix some deep-seated issues their elder siblings have faced? Or would the throat still cough differently again?

NO MORE TONE-DEAF LMS

The challenge with existing AI models was that they first had to convert speech to text through direct or multimodal approaches, take the input for synthesising it with a language model, and convert it all with text-to-speech techniques. This consumed time. This took up compute power. This needed data inputs. But above everything else, this process still missed out on subtle aspects like pitch, tone, emotion and other sub-text areas of the human voice. Not to mention the sheer diversity of accents, dialects and vernacular speech, especially in a multi-cultural country like India.