Conversational AI Training With Intermediate Audio Representations
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Solution Overview
Problem
Customizing or tuning Automatic Speech Recognition (ASR) models for new domains is challenging due to the limitations of available data and computational resources, and existing methods for generating audio-text pairs are computationally expensive and result in reduced model performance.
Innovation Solution
Hybrid language models are used to convert textual data to intermediate audio representations, which are then processed by ASR models, allowing for training and fine-tuning without requiring significant architectural changes or computational resources, and enhanced using Generative Adversarial Networks (GANs) to improve detail in these representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Text-To-Speech (TTS) models are used to synthesize audio from text to generate audio-text pairs, then training data can be generated without manual transcription, but the computational cost increases significantly and the synthetic audio causes reduced model performance due to mismatches with natural audio
Solution Approach 1:
The patent uses a frozen ASR model to copy and transform text input into intermediate audio representations (spectrograms) that mimic the internal representation of real audio, rather than generating actual synthetic audio waveforms. This copying approach at the representation level avoids the computational burden of full TTS synthesis while maintaining compatibility with ASR training.
Solution Approach 2:
The patent introduces an intermediate audio representation (spectrogram) as a mediator between text and the ASR model. Instead of directly using text or full synthetic audio, the frozen ASR model's audio encoder transforms text into an intermediate spectrogram format that bridges the gap, enabling training without the harms of direct TTS synthesis.
2Ease of manufacture
If TTS models are used to generate audio-text pairs for training, then manual transcription requirements are reduced, but model performance decreases due to mismatches between synthetic and natural audio
Solution Approach 1:
The patent changes the parameter space from generating raw audio waveforms to generating spectrogram representations. By transforming the output of the text-to-audio process from time-domain waveforms to frequency-domain spectrograms, the system achieves better compatibility with ASR model expectations and maintains higher performance while still enabling automated data generation.
Solution Approach 2:
The frozen ASR model's audio encoder is used to copy the transformation process that normally occurs during real audio processing. This copying of the encoding process allows text to be transformed into spectrograms that match the statistical characteristics of real audio representations, improving model performance.
3Reliability
If end-to-end ASR systems use external language models to improve performance, then recognition accuracy improves, but computational resources are consumed significantly
Solution Approach 1:
The frozen ASR model serves multiple functions: it acts as both the audio encoder to generate intermediate representations and as the ASR model to be trained. This multi-functionality eliminates the need for separate external language models while still enabling effective training through the use of text input transformed into audio representations.
Data Source
AI summary
In various examples, first textual data may be applied to a first MLM to generate an intermediate speech representation (e.g., a frequency-domain representation), the intermediate audio representation and a second MLM may be used to generate output data indicating second textual data, and parameters of the second MLM may be updated using the output data and ground truth data associated with the first textual data. The first MLM may include a trained Text-To-Speech (TTS) model and the second MLM may include an Automatic Speech Recognition (ASR) model. A generator from a generative adversarial networks may be used to enhance an initial intermediate audio representation generated using the first MLM and the enhanced intermediate audio representation may be provided to the second MLM. The generator may include generator blocks that receive the initial intermediate audio representation to sequentially generate the enhanced intermediate audio representation.