Amazon's research team has developed what is considered the most comprehensive text-to-speech artificial intelligence model to date. Dubbed the Big Adaptive Streamable TTS with Emergent Abilities (BASE TTS), it showcases emergent capabilities that enhance its natural speech performance.
This innovative model can be considered a milestone in technology and could potentially catapult text-to-speech models out of the all-too-familiar uncanny valley. Continue reading to know more about it.
The Pursuit Of Enhancement In Large Language Models
The core objective of the researchers was to mirror the substantial increase in capabilities observed in language models when they exceed a particular size. It's observed that as large language models (LLMs) expand, they become more resilient and versatile, executing tasks far beyond their initial training scope.
However, it's essential to understand that it doesn't equate to consciousness development, but rather a significant performance boost in specific conversational AI tasks.
The Features Of The BASE TTS Model
The research team at Amazon AGI developed the BASE TTS model, in anticipation of these enhancements. With 980 million parameters and utilization of 100,000 hours of public domain speech, this model is hailed as the largest in its category.
To ascertain the point at which these emergent abilities start to manifest, the research team also curated smaller models for comparative purposes.
Breaking Down The Performance
Upon evaluation, the medium-sized model exhibited considerable improvements, showcasing a range of emergent skills. While the standard speech quality increased marginally, it demonstrated proficiency in handling the following:
- compound nouns;
- emotions;
- foreign words;
- paralinguistic features;
- punctuation usage;
- questions formation; and
- syntactic complexities.
Such elements often baffle text-to-speech engines, leading to word mispronunciations, skipping words, and odd intonation. In contrast, the BASE TTS model superseded its counterparts in tackling these complexities.
Contributing Factors And Prospects
The BASE TTS model's architecture, size, and comprehensive training data collectively contribute to its superior performance. However, it is still vital to remember that this model is in the experimental phase.
Future research will focus on determining the point of emerging ability and devising effective methods to train and deploy the resulting model.
Advantages And Potential Misuse
Another feature of the BASE TTS model is its "streamable" nature. It isn't required to generate complete sentences at once but can produce speech incrementally at a reasonably low bit rate. This characteristic combined with the model's ability to encode speech-related metadata like emotionality and prosody, could usher in a revolutionary transformation in the text-to-speech domain.
While the potential misuse of this technology by unscrupulous entities has led to a refrain from publishing the model's source and other data, the benefits, especially in terms of accessibility, are hard to ignore and will likely be replicated and built upon.
