Google AI has introduced a new spoken language model called “Spectron” that processes spectrograms as both input and output. Spectrograms are visual representations of the spectrum of frequencies of a signal. The model uses pre-trained encoders and decoders to transcribe and generate text and speech continuations, improving the quality of synthesized speech. However, the model has limitations in generating long speech utterances and running text and spectrogram decoding in parallel. The team plans to focus on developing a parallelized decoding algorithm in the future.
Spectron: The First Spoken Language AI Model that Processes Spectrograms
Spectron is a groundbreaking AI model developed by Google AI and Verily AI. It directly processes spectrograms, which are visual representations of sound frequencies over time. This unique approach allows Spectron to transcribe and generate text continuations, acting as an ‘intermediate scratchpad’ for audio generation. It eliminates biases and maintains representational fidelity, resulting in high-quality speech synthesis.
Practical Applications and Value
Spectron offers practical solutions for various industries and tasks:
- Enhancing Productivity: Spectron can automate transcription tasks, saving time and improving efficiency in industries such as legal, healthcare, and media.
- Improving User Experiences: By generating contextually relevant and coherent text, Spectron can enhance chatbots, voice assistants, and customer service interactions.
- Advancing Research and Development: Spectron’s ability to understand and generate text opens doors for advancements in natural language processing, speech recognition, and audio analysis.
How It Works
Spectron’s architecture involves a pre-trained speech encoder and language decoder. The encoder processes speech utterances and generates linguistic features, which serve as input for the decoder. The decoder is optimized to minimize cross-entropy by generating text and speech continuations.
The researchers also used the same architecture to decode intermediate text and spectrograms, resulting in improved speech synthesis quality.
Limitations and Future Development
Spectron has some limitations:
- Generating long speech utterances can be time-consuming due to the need to generate multiple spectrogram frames.
- Text and spectrogram decoding cannot run in parallel.
However, the team is actively working on developing a parallelized decoding algorithm to address these limitations in the future.
To learn more about Spectron, you can read the paper and blog post by the researchers.
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